However, with the introduction of AI for leaders, I began transforming data into actionable insights much faster without sacrificing depth. That is why I decided to fully embrace AI and use it as a lever to amplify my strategic leadership. 

In this article, I share how I built my own digital staff with multiple AI agents that support me in vision, decision-making speed, and proximity to both my team and our clients.

My Turning Point

At Zup Innovation, we are constantly challenged to reflect on questions such as:

• How can I use AI to have a greater impact on the organization?
• How can I become an AI-first professional?
• How can I integrate AI into my routine to generate real value?

These questions have accompanied us daily and led me to rethink my own professional performance.

Looking at market data makes their relevance even clearer:

• According to an OpenAI study, 80% of the workforce will have at least 10% of their tasks impacted by LLMs, while about 19% of workers may see at least 50% of their functions affected.
• On top of that, Gartner predicts that by 2028, at least 15% of work decisions will be made autonomously by AI agents.

So, if as a leader you only use AI for simple tasks like writing emails, you are leaving most of its potential untapped.

What is AI for Leaders? 

AI for Leaders is the integration of specialized agents into the executive routine to generate deep analyses, maintain alignment with strategy and culture, and reclaim time for strategic focus.

Instead of creating a generic “super-assistant,” leaders can orchestrate a digital staff of AI agents to:

• cross-reference business, people, market, and financial data,
• offer recommendations tailored to their specific context, and
• learn from their organization’s culture and priorities.

How StackSpot Empowers Leaders with AI

At Zup, we rely on the StackSpot platform to explore Agentic AI architectures with multiple autonomous agents. So, what I had to do was roll up my sleeves and get to work.

The ease with which I created these agents was surprising. Each had a clear role, accessed data through Retrieval-Augmented Generation (RAG), and learned with contextual memory.

StackSpot AI is a multi-agent platform designed for the full development and technical decision-making cycle. Its hyper-contextualized AI:

• understands your code, documents, business rules, and culture,
• increases productivity while maintaining good practices and standards,
• uses RAG and contextual memory for accurate responses,
• enables collaboration between agents and people in real workflows,
• improves the developer experience and strengthens collaboration between technical and business teams.

Is your company still building software the old way? Discover StackSpot AI Agents!

Architecting a Digital Staff for Leaders with AI

With a multi-agent and hyper-contextualized platform like StackSpot AI, leaders can move beyond simple task automation and amplify their core leadership capabilities. I currently use six agents that have transformed both my daily work and my impact on the organization.

1. Financial Scenario Analyst: projects impacts, conducts stress tests, and recommends actions to protect margin and cash.

2. Strategy and Culture Guardian: checks whether initiatives align with objectives and cultural pillars, reducing deviations.

3. Market and Competition Radar: monitors trends, competitors, and emerging technologies while identifying risks and opportunities related to your plan.

4. People and Climate Pulse: interprets eNPS, feedback, and OKRs, and suggests engagement and development actions.

5. Executive Communication Coach: adjusts tone and clarity in critical messages, presentations, and sensitive announcements.

6. 1:1 and Career Mentor: structures conversations, suggests action plans and development content, and tracks progress.

For example, in a recent strategic meeting, my “Market and Competition Radar” agent synthesized the trends, risks, and opportunities already tied to our strategy and cultural pillars. The impact was immediate: we shifted from simply reacting to discussing how to position ourselves proactively.

Benefits for Leaders

The value of adopting these AI solutions goes far beyond efficiency. They act as partners in decision-making, helping leaders connect dots across complex areas of the business while gaining back valuable time for people and strategy. The key benefits include:

• deeper, more integrated insights that connect diverse areas and sources,
• safer decisions, already contextualized to the business,
• hours freed up to spend with the team and clients, and
• consistent governance with decision trails and learnings captured.

Discover the impact of StackSpot AI agents on the development cycle. Request a demo today!

Tips for Creating a Staff of AI Agents

Building a digital staff doesn’t have to be overwhelming. By starting small and focusing on clarity of purpose, leaders can quickly see results and scale from there. These four steps will help you shape an effective team of AI agents:

Define clear executive objectives.

For example: reduce preparation time by 50%, anticipate market risks, or improve the quality of cross-functional decisions.

Connect context and internal sources.

Include strategy documents, policies, indicators, playbooks, and rituals. Separate sensitive contexts by agent and area, and limit access to the Knowledge Source.

Create your agents and define their roles.

Set clear purposes, missions, and limits. Standardize recurring tasks such as committee analysis, eNPS reading, market scanning, and financial briefings.

Test, measure, iterate, and scale.

Run pilots in specific teams, collect metrics, refine prompts and sources, then expand to other leaders with governance for versions, responsibilities, and auditing.

Frequently Asked Questions About AI for Leaders

As with any new technology, leaders often have questions about how AI agents fit into their daily routines and responsibilities. Here are some of the most common ones:

What is AI for Leaders?

It is the coordinated use of AI agents to amplify executive decision-making, accelerate analyses, and strengthen culture and strategy.

What are its practical benefits?

Faster and better-informed decisions, an integrated view of the business, time reclaimed for strategic focus, and stronger alignment across areas.

Will AI replace or augment leadership?

It will augment. Agents do not decide alone; they expand vision, accelerate analyses, and give leaders more confidence in their actions.

Where do I start?

Define your objectives, connect reliable sources, create a few agents with clear roles, run pilots, and scale with governance.

AI for Leaders: Transformation and Real Impact 

AI for leaders is not about automating what already exists but amplifying what makes leadership unique: discernment, direction, and people development.

With a staff of hyper-contextualized agents in StackSpot AI, scattered data can be transformed into coordinated decisions, freeing leaders to focus on what matters most.

After all, the future of leadership isn’t human versus AI. It is human with AI.

References

• Top 10 Strategic Technology Trends for 2025 | Gartner
• An Early Look at the Labor Market Impact of Large Language Models | OpenAI

From social media and corporate systems to how we build software, this technology has proven to be a powerful ally in content creation, process automation, and innovation across industries.

In this article, we will demystify Generative AI, exploring its key models, applications, and how it changes the way we interact with the world.

Get ready for a practical and insightful guide to using this technology effectively and creatively.

1. What is Generative Artificial Intelligence (Gen AI)?

Generative Artificial Intelligence, or Gen AI, is a technology that’s revolutionizing how we create content and approach work. Imagine a system capable of producing new ideas and outputs like text, image, music, or video. Well, that’s exactly what Gen AI does! It learns from existing data and generates original results, offering creative solutions to complex problems.

Its impact spans sectors such as healthcare, marketing, and software development, creating tangible value and ROI.

On top of that, Generative Artificial Intelligence also holds significant economic potential, allowing companies to explore new forms of engagement and innovation. According to consulting firm Bain & Company, the AI products and services market could reach $780 billion to $990 billion by 2027.

2. What is the Difference Between Traditional AI and Generative AI?

Artificial Intelligence (AI) broadly refers to systems that perform tasks requiring human-like intelligence, such as recognizing patterns, making decisions, or understanding language. Within this field, we can distinguish between two types of AI: traditional and generative.

Traditional AI focuses on interpreting and acting on existing data. Using techniques such as Machine Learning and Deep Learning, it identifies patterns, classifies information, and makes predictions.

Recommendation systems like Netflix or Spotify, for example, use traditional AI to analyze your behavior and suggest relevant content. This form of AI emulates human cognitive functions by identifying patterns and making predictions. However, traditional AI doesn’t create anything new; its insights are limited to existing inputs.

Generative AI, on the other hand, does create. It goes beyond analysis and generates new, original content. Tools like ChatGPT are examples of this technology, capable of transforming simple prompts into entirely new outputs.

3. How does Generative AI Work?

Understanding how Gen AI works helps to clarify its capabilities. It typically involves four components:

Foundation Models

The first is the development of foundation models. These are trained on massive volumes of unlabeled data such as text, images, video, and audio to learn complex patterns and relationships. The result is a model that can generalize across tasks.

Fine-Tuning

Once the foundation is built, the model is refined using labeled data representing tailored to specific cases. For instance, if used in customer service, the model might be fine-tuned on real conversation logs to improve accuracy and context awareness.

Interaction Through Prompts

Users engage with Gen AI through prompts, which are questions or instructions that guide the model’s response. The quality of these prompts directly impacts the relevance of the output. This is why Prompt Engineering is becoming a key skill.

Continuous Improvement

Finally, to keep outputs relevant and accurate, Gen AI models are regularly evaluated, updated, and improved. Feedback from users also helps refine the models over time.

4. What are the benefits of Generative AI?

Increased Productivity

Gen AI is driving change at every level. Among its top benefits is increased productivity. Gen AI automates time-consuming everyday tasks like writing, editing, summarizing, or coding, saving hours and boosting team efficiency.

Personalization

It also enables highly personalized content and user experiences, improving customer engagement and satisfaction.

Solving Complex Problems

Generative Artificial Intelligence can be a valuable ally in solving complex problems. By analyzing vast amounts of information, it can generate meaningful insights and propose innovative solutions.

This is particularly impactful in fields such as scientific research and medical diagnostics. For instance, Gen AI can examine medical imaging like CT scans and MRIs to detect anomalies with a level of accuracy that may surpass human capabilities. In research environments, it can explore and interpret data from diverse sources, helping researchers identify patterns, generate hypotheses, and accelerate discovery.

Reduction of Operational Costs

Gen AI-powered automation can significantly reduce costs in areas such as customer service and technical support. Tools like chatbots and virtual assistants can be deployed to answer customers and even solve simple demands. They can also be set up to send users personalized messages and offers.

Optimization of Business Processes

Additionally, Generative AI helps optimize business processes by applying machine learning across different units or departments. As an illustration, it can generate synthetic data to improve model training, enhancing decision-making and operational efficiency.

Reduced Time-to-Market

Finally, it can accelerate time-to-market by, among other things, enabling faster prototyping and testing at a lower cost. This allows businesses to launch new products and services more quickly while consuming fewer resources.

5. What are the Risks of Generative AI?

Generative AI fuels innovation, but it also presents important risks that must be addressed with care and responsibility to ensure the safe and ethical use of the technology.

Privacy and Security

Because generative models require vast datasets to be trained and refined, they often handle sensitive or proprietary information. This opens up the possibility of unintentional data exposure or misuse. For example, an AI trained on confidential company documents or user interactions might accidentally reveal private data in its output. 

There is also the growing risk of malicious use, such as the creation of deepfakes or realistic fake content designed to mislead, manipulate, or defraud individuals and organizations. To mitigate these risks, companies must adopt strict security protocols, invest in robust data anonymization strategies, and ensure proper access controls are in place throughout the AI lifecycle.

Regulatory Compliance

The rapid pace of Gen AI adoption has outstripped the development of comprehensive legal and ethical frameworks. As a result, many companies are left navigating a patchwork of local, national, and international regulations. Compliance with existing data protection laws is crucial; however, new AI-specific policies and guidelines are emerging globally, and organizations must stay up to date to avoid legal exposure. 

Intellectual Property

Intellectual property is also at stake. Because Gen AI can generate new content, that raises complex copyright and intellectual property issues. Organizations must create clear policies around the use and protection of AI-generated content and ensure respect for third-party rights.

Bias and Discrimination

AI models can inadvertently perpetuate biases contained in the data on which they were trained. A well-known example occurred in 2018, when a hiring tool used by Amazon showed discriminatory behavior against female candidates, a result of being trained on historically male-dominated data.

To prevent similar issues, organizations must implement controls to detect and mitigate biases, making sure their AI outputs are fair and ethical.

6. What are the Limitations of Generative Artificial Intelligence?

Although Generative AI offers remarkable capabilities, it also has inherent limitations that must be understood to ensure its responsible and effective use.

Below, we look at some of its main limitations:

Accuracy and Reliability

Even the most advanced Gen AI models are not immune to error. These systems generate outputs based on patterns found in the data they were trained on, which means they may reproduce or even amplify inaccuracies, inconsistencies, or biases present in that data. 

In certain cases, AI may “hallucinate,” producing outputs that are entirely fabricated but presented with confidence and fluency. This risk becomes especially critical in sensitive domains such as healthcare, finance, or legal services, where misinformation can have serious consequences. 

To mitigate this, human oversight is essential. Organizations should implement review protocols to validate outputs before they are used in production environments, ensuring that the AI serves as a support tool rather than a final authority.

Creativity Constraints

While Gen AI can simulate creativity by producing content that appears original, its “imagination” is bound by the data it has been trained on. True originality is still a human domain, especially when it involves emotional understanding.

One way to overcome this limitation is to adopt a co-creation model, in which AI augments human creativity rather than replacing it. By leveraging Gen AI as a collaborative partner, creators can iterate more quickly, test ideas efficiently, and ultimately push the boundaries of what’s possible — without losing the human touch.

7. What are the Generative Artificial Intelligence Models?

Different generative AI models are expanding the limits of what can be achieved, from generating text to creating images and sounds. Below are the main model types, how they work, and where they are most effective.

Diffusion Models

How they work: These models begin by adding noise to the training data until it becomes unrecognizable. Then, the algorithm is trained to reverse this process and recover a coherent and meaningful output. 

Applications: Primarily used for image and video generation, diffusion models allow precise control over the creative process and are known for producing high-resolution results.

Generative Adversarial Networks (GANs)

How they work: GANs consist of two neural networks operating in a competitive setup. The generator creates synthetic data, while the discriminator evaluates whether the data is real or fake. This back-and-forth improves the model’s ability to generate increasingly convincing outputs.

Applications: Widely applied in image generation, style transfer, and data augmentation. GANs are often used in creative industries and synthetic media.

Variational Autoencoders (VAEs)

How they work: VAEs rely on two neural components, a neural encoder and a decoder, which work together to learn a compact representation of the data. From this compressed form, the decoder can reconstruct variations that resemble the original inputs.

Applications: Particularly effective for generating images and videos that preserve the essence of the training data. Also valuable for data compression and representation learning.

Transformers

How they work: Transformers use a self-attention mechanism to understand the context and relationships between elements in a sequence, such as words in a sentence. This architecture allows them to generate coherent and contextually appropriate outputs.

Applications: Core to Natural Language Processing (NLP), transformers power models like GPT (Generative Pre-trained Transformer), which are widely used in chatbots, translation tools, summarization systems, and virtual assistants.

Large Language Models (LLMs)

How they work: LLMs are large-scale transformer-based models trained on extensive corpora of text data. They predict and generate natural language based on context and user input.

Applications: Found in many AI-powered tools, including content generators, conversational agents like ChatGPT, and customer support solutions that rely on natural language understanding.

8. What is the Best Generative AI Tool?

As with many challenges in technology, the answer to this question is “it depends.” To understand which Artificial Intelligence application is ideal for your case, ask yourself a few questions:

• What do I want to achieve?
• In what environment will this be used?
• What results am I expecting?
• Do I need to consider any significant time or budget constraint?

9. How to Adopt Generative AI?

Adopting generative AI is a strategic step that can transform business processes and outcomes. Below is a practical guide for implementing this technology effectively and securely.

Clear Communication

When introducing generative AI, it is essential to maintain open and honest communication with all stakeholders. Doing so not only reduces uncertainties but also ensures everyone is aligned with the organization’s goals.

Establish dedicated channels to quickly answer questions and encourage cross-team collaboration. Treat AI adoption as a collective initiative to maximize its positive impact.

Balancing AI and Human Collaboration

Even the most advanced AI requires human input. People play a critical role in training models, providing contextual interpretation, and guiding ethical and responsible use.

Professionals must be equipped to supervise, adjust and validate AI behavior so that the technology complements rather than replaces human expertise.

Start with Internal Applications

Deploying generative AI in internal workflows is a strategic first move. It creates a controlled environment for testing, refining, and learning.

This approach enables teams to develop confidence with the technology before introducing it to external-facing operations. When the time comes, thoroughly customized and tested internal models help deliver a better user experience.

Double Attention to Security

Information security must be prioritized when adopting generative AI.

Protect Your Data

Implement strong protections to prevent unauthorized access to sensitive information. This includes applying data masking and removing personally identifiable information (PII) before using data in AI model training.

Involve the Security Team from the Beginning

Include security professionals early in the process. Doing so allows your team to address risks from the design stage through to deployment.

Prepare for Cyber Threats

New threats are emerging as generative AI advances. These include deepfakes and other tools used in social engineering attacks. Mitigate these risks by developing robust controls and updating your security policies to address AI-related vulnerabilities.

Work with Reliable Suppliers

If you use third-party AI tools or services, confirm that they do not use your data for purposes outside your organization. Build clear contracts and maintain transparent communication with suppliers to protect your data and minimize misuse.

Test a Lot

Develop thorough testing processes to assess the quality and reliability of generative AI outputs. combine automated and manual tests to simulate a wide range of scenarios. 

Beta testing groups can provide valuable insights and surface edge cases that help fine-tune the model before broader deployment. Testing is key to ensuring that your implementation supports strategic goals.

Be Transparent

Transparency builds trust and supports ethical AI adoption. After all, to ensure efficient adoption, there must be trust in the technology and its intentions.

This level of clarity is not only the best practice but also aligns with global guidelines such as the OECD recommendations on AI, which are included in Brazil’s Artificial Intelligence Strategy (EBIA).

Continuous Monitoring

Once generative AI is in place, continuous monitoring is essential. Regular oversight ensures the system performs as expected, detects anomalies early, and responds to any new risks or failures.

Ongoing evaluation also enables teams to make improvements that keep the solution aligned with evolving business needs.

Recommended reading: Challenges in AI adoption: How to transform obstacles into competitive advantages

10. What is the Future of Generative AI?

To understand the future of generative AI, it helps to look at Gartner’s predictions:

• By 2026, 75% of companies will use Generative Artificial Intelligence to create synthetic customer data. This represents a significant leap from less than 5% in 2023. Synthetic data refers to information generated through algorithms, simulations, or computational models rather than collected from real-world human activity.
• By 2027, more than half of the generative AI models adopted by companies will be designed for specific industries or business functions. In 2023, this figure was approximately 1%.
• By 2028, 30% of GenAI implementations will be optimized using energy-efficient computing methods. This shift will be driven by sustainability commitments and the need to reduce the environmental impact of AI operations.

Conclusion

We are undoubtedly just beginning to explore the full potential of Generative Artificial Intelligence. From automating tasks to redefining entire industries, this technology continues to reshape what is possible in the digital world.

However, as with any disruptive innovation, there are challenges to overcome. Responsible governance, robust security practices, and transparent implementation will be key to building trust and ensuring long-term success.

Now, we want to hear from you! What other questions do you have about Generative Artificial Intelligence? Leave a comment and let’s continue the conversation!

References 

• What Is Generative AI? | AWS
• What is generative AI? Examples and Use Cases| Google Cloud
• Managing the risks of generative AI – PwC
• When Not to Use Generative AI | Gartner
• Generative AI: What Is It, Tools, Models, Applications and Use Cases | Gartner
• Gartner Generative AI Predictions for 2024-2028 | Gartner
• Generative AI: What CIOs and CTOs need to know | StackSpot AI
• Gen AI in legacy modernization: 6 practices for adopting | StackSpot AI
• O que é ‘alucinação’ de inteligência artificial – BBC News Brasil
• Tipos de IA Generativa: explorando as inovações e aplicações – Meta
• Dados sintéticos vs dados naturais: o limite da inteligência artificial no treinamento de modelos – Coluna Marcos Bonas, TI INSIDE Online
• Qual o impacto da IA Generativa? – Portal Telemedicina
• IA Generativa: o que é, como funciona e aplicações | Distrito
• IA Tradicional e IA Generativa: qual a diferença e qual a melhor? | Zendesk
• IA Generativa: o que é, vantagens e exemplos – Sankhya
• Conheça os 4 maiores riscos da IA generativa e saiba como mitigá-los – InfoMoney
• Guia Completo Sobre Inteligência Artificial Generativa – Data Science Academy

With a streamlined architecture and fewer parameters, SLMs can handle natural language processing tasks with impressive efficiency and much lower computational demand, especially when designed for specific use cases.

This article explores the key characteristics of SLMs, when they’re most effective, and why they’re becoming central to the evolution of AI.

What are Small Language Models (SLMs)?

As the name suggests, Small Language Models, or lightweight models, are significantly smaller than Large Language Models (LLMs).

While LLMs may contain billions or even trillions of parameters, SLMs typically work with millions or even just thousands. This compactness makes them more accessible, in that it enables training and executing them on modest hardware.

What’s more, when trained with targeted datasets, tailored for a specific use case, SLMs can deliver accuracy and performance that rival their larger counterparts.

Examples of Small Language Models

A growing number of companies, from Big Tech to startups, are rolling out SLMs, and the list continues to grow. Some of the most recognized examples include:

• Some models within Meta’s LlaMa family
• Microsoft’s Phi
• Some models within Alibaba Cloud’s Qwen family 
• Mistral Nemo, developed by Mistral AI and NVIDIA
• DistilBERT, MobileBERT, and FastBERT, developed by Google

You’ll also find many open-source SLMs on HuggingFace, with community reviews to guide you.

How do SLMs Work?

SLMs are built around three main characteristics:

1 – Architecture

SLMs use simplified neural network designs with far fewer parameters than LLMs. This compact structure allows them to focus on domain-specific tasks while consuming much less computational power, often delivering more targeted results.

2 – Next Word Prediction

Just like LLMs, SLMs are trained to predict the next word in a text sequence based on a set of patterns. This seemingly simple approach is highly effective and sits at the core of all language model functionality.

3 – Transformer Foundation

SLMs are based on the Transformer architecture, which uses self-attention mechanisms to understand word relationships within a sentence. This enhances text coherence and enables contextual, accurate responses.

Five Key Benefits of Small Language Models

SLMs bring a number of advantages that make them a go-to option for many organizations:

1 – Accessibility and Cost Efficiency

SLMs can be trained and deployed without expensive infrastructure. This opens the door for smaller teams and startups to explore powerful AI applications without breaking the bank. 

2 – Customization and Flexibility

Thanks to their compact size, SLMs can be easily adapted to niche tasks across specialized domains like healthcare, education, and customer support. This makes them especially effective in targeted use cases.

3 – Fast Inference and Low Latency

With fewer parameters to process, SLMs deliver faster responses. This is perfect for real-time applications like virtual assistants and chatbots. 

4 – Enhanced Privacy and Security

SLMs can be deployed on private clouds or on premises, offering more control over data and reducing exposure to third-party systems. This is a major plus in highly regulated sectors like finance or healthcare.

5 – Sustainability

By using less processing power, SLMs contribute to lower energy consumption, helping to reduce the environmental impact of AI development.

When are SLMs Not Enough?

While Small Language Models bring several advantages, they also come with limitations that must be considered, especially in use cases that demand high precision or a deeper understanding of language.

Limited Capacity for Complex Language Understanding

Unlike LLMs, which are trained on extensive and diverse datasets, SLMs operate within a narrower scope. This reduced exposure may limit their ability to interpret linguistic nuances, subtle context shifts, or intricate semantic relationships. As a result, their outputs may oversimplify content or miss critical context, particularly in sophisticated dialogues or domain-specific applications.

Handling Complex Tasks

SLMs are designed for efficiency and specialization, but this also means they may lack the breadth and processing depth required for highly complex problem-solving. In fields where precision and completeness are essential, such as medical diagnostics, legal reasoning, or scientific modeling, SLMs may fall short, increasing the likelihood of errors or incomplete outputs.

Limited Generalization

Because of their compact structure and focus on specific tasks, SLMs are less capable of generalizing across diverse topics and scenarios. While this focus makes them efficient for targeted applications, it also limits their adaptability. In tasks that require creative reasoning or flexible knowledge transfer across domains, they may generate more constrained or generic responses.

Bias and Accuracy Risks 

Like all AI models, SLMs are susceptible to biases embedded in their training data. Since they often inherit these datasets from larger models, they can reflect and even amplify unwanted patterns. This can affect the quality, fairness, and accuracy of their outputs. For organizations adopting SLMs, it’s essential to validate results and implement oversight mechanisms to mitigate these risks, just as one would with any Gen AI solution.

LLMs vs SLMs: Which One to Choose?

Choosing between an LLM and an SLM is not a one-size-fits-all decision—and it can directly impact the results of your project or business. Each model type offers distinct advantages and is better suited to specific contexts, depending on the complexity of the task, the available infrastructure, and the desired balance between performance, cost, and control. 

To help guide this decision, the table below compares the two approaches across key criteria:

Choosing the Right Model

In general terms, SLMs are the better choice for tasks that are narrow in scope, cost-sensitive, and privacy-focused. Their lower resource requirements and adaptability make them ideal for use cases that demand efficiency, fast deployment, and greater control over data handling.

LLMs, by contrast, excel in scenarios that require extensive reasoning, broader domain coverage, or the ability to process large and complex datasets. They are well-suited for applications where flexibility and depth of understanding are essential.

In practice, however, most organizations face a range of challenges that cannot be addressed by a single model type. This is why adopting a hybrid strategy that combines LLMs and SLMs can lead to more intelligent orchestration and more effective outcomes across the board. 

Where SLMs Make the Biggest Impact

The flexibility of Small Language Models is especially valuable in sectors where language and data play a central role. By adapting to specific tasks, contexts, and terminologies, SLMs deliver targeted results that reflect the unique needs and realities of each business.

In healthcare, they assist in diagnostics and medical record analysis, enabling a more accurate and personalized approach.

In education, they support personalized learning and individual student feedback, allowing for more dynamic and effective teaching.

In customer service, they power efficient and natural interactions in virtual assistants, improving the user experience.

In manufacturing, they enhance predictive maintenance and optimize processes, proactively preventing equipment failures.

Most Common Use Cases

Q&A Systems: These models can deliver accurate and detailed responses for support agents or self-service platforms.

Summarization: SLMs can condense large volumes of information into digestible insights, allowing for much faster analysis.

Conversational AI: Because they can interact in natural and engaging ways, SLMs are widely used in context-aware chatbots and virtual assistants, improving the user experience across different platforms.

Making SLMs Smaller and Smarter

Small Language Models (SLMs) are designed using advanced optimization techniques that make them compact, fast, and efficient without significantly sacrificing accuracy. These techniques are essential for developing AI solutions tailored to specific use cases, especially when computational resources are limited.

Here are the main approaches that enable this efficiency:

Knowledge Distillation

A larger model (the “teacher”), transfers its learning to a smaller one (the “student”). By mimicking the teacher’s outputs, the student retains much of the original model’s accuracy while using fewer parameters and less processing power. This is especially effective for domain-specific tasks.

Pruning

Pruning removes parameters or neurons that contribute little to performance, making the model lighter and faster. When applied carefully, this technique preserves accuracy while reducing complexity. However, aggressive pruning can impact results, so it must be used strategically.

Quantization

This method reduces numerical precision (by converting 32-bit values to 8-bit, for example), lowering memory usage and improving speed. It’s particularly useful for deploying models on devices with limited resources, like smartphones, while keeping performance largely intact.

Low-Rank Factorization

Large weight matrices are broken into smaller ones, simplifying computations and reducing parameter count. Although this typically requires fine-tuning afterward, it can render the model much more efficient without undermining its capabilities.

Together, these techniques allow SLMs to deliver high performance in a lightweight format, making them ideal for focused, cost-effective AI applications.

Specific Training = Specific Results

SLMs excel when trained with tailored data like clinical notes or financial transactions. This focus enables precision in domains where general-purpose LLMs might falter, making these lightweight models ideal for environments where precision is paramount.

SLMs at Work with StackSpot AI

StackSpot AI is a multi-agent platform that supports both LLMs and SLMs. This means organizations can configure their accounts to orchestrate across different models and align AI capabilities with their specific needs.

The platform also enables interactions between agents powered by different models, ensuring flexibility, precision, and scalability across use cases. 

Small Language Models: Efficient by Design, Ready for Scale

SLMs are practical, agile, and sustainable. They offer an effective solution for organizations that need to deliver fast results, optimize costs, and maintain control over sensitive data. Agile and adaptable, they have become increasingly valuable tools in a dynamic, constantly evolving business environment.

LLMs are still essential for complex, large-scale tasks. But when the goal is speed, specificity, and efficiency, SLMs are the way to go. Chances are, your organization will benefit from both.

Already working with Small Language Models? Tell us about your experience in the comments!

References

• O que são os Pequenos Modelos de Linguagem (SLMs)? | Blog Conteudize
• Pequenos modelos de linguagem: o que são e porque o setor financeiro deveria se importar com eles? | Blog Red Hat
• Small language models are gaining traction — here’s what you need to know – CB Insights Research
• Orchestrating LLMs and SLMs: How to Turn AI Models into a Competitive Advantage – StackSpot AI
• Small Language Models: A Guide With Examples – DataCamp
• Top 15 Small Language Models for 2025 – DataCamp
• O que são Pequenos Modelos de Linguagem (SLMs)? – Microsoft Azure
• What are Small Language Models (SLM)? – IBM
• Unlocking the Power of Small Language Models (SLMs) – mroads blog
• Small Language Models (SLMs) – Vantagens e Desvantagens – Data Science Academy
• SLMs vs LLMs: What are small language models? | Blog Red Hat

In this article, we’ll take a closer look at the role of Gen AI in software development, exploring its impact, how to overcome key challenges, and which strategies can help mitigate risks while maximizing the value of this powerful technological accelerator.

Note: This article draws from structured research, empirical observations, and the author’s extensive expertise.

The Impact of Gen AI on Software Development

The earliest applications of Gen AI in software development involved code assistants. These were tools that answered questions, suggested code snippets or files, and supported autocompletion functions.

These assistants quickly evolved to allow file creation and editing, leading to more advanced AI agents capable of planning work, interfacing with external tools, and independently creating, modifying, and deleting code and files.

As time went on, these agents gained even more capabilities, to the point where they could produce entire websites or applications with no human input.

According to the 2024 DORA Report, most teams are already investing in Gen AI: 89% use it to enhance their products, while 76% rely on it as an everyday productivity tool.

We’ve moved beyond the hype. Companies now seek measurable outcomes from their AI efforts, and software development is no exception. The 2025 State of the CIO survey shows that, for 68% of IT leaders, AI has already reshaped operations and is delivering tangible business value.

Gen AI is helping developers boost productivity, generating quality documentation, writing tests, and speeding up code creation and review.

For instance, the 2024 Stack Overflow Developer Survey featured an entire chapter on Artificial Intelligence. More than 35,000 development professionals worldwide shared how AI has become part of their development workflow:

Still, there are lingering concerns: intellectual property issues, performance and quality in production, and even fears that AI might take away the “fun” parts of development or threaten jobs.

Key Use Cases for Gen AI in Development

Gen AI unlocks powerful accelerators that, when applied well, can boost productivity and code quality. Here are some of the most impactful use cases:

1. Write Documentation

Development teams often lack the time or discipline to keep documentation up to date. Gen AI can generate materials like Mermaid diagrams, Javadoc or OpenAPI documentation, architecture decision records (ADRs), and even C4 architecture diagrams.

2. Write Unit and Integration Tests

Similar to documentation, tests are often neglected. AI agents can create and maintain a wide range of tests, from basic unit tests to mutation and even Test-Driven Development (TDD) or Behavior-Driven Development (BDD) approaches.

Itaú Unibanco, for example, doubled its accessibility test coverage using StackSpot AI.

3. Create Simpler Code

Tasks like writing getters and setters, build scripts, or CRUDs can consume valuable developer time. Gen AI can handle these with quality and speed.

Among other things, you could ask your AI agent to implement CRUD for a new entity using a simple set of business rules.

4. Review Code Changes

According to Harvard Business Review, improving code post-creation ranks as the 8th most frequent Gen AI use case in 2025. AI agents can assist in code reviews, reducing reliance on senior staff and speeding up PR approvals.

Understanding the Risks

Using Gen AI without the right context or at an unsustainable pace can easily backfire. Instead of boosting efficiency, it might actually slow things down, especially when the generated code can’t be used in production. To avoid this, it’s important to understand the key risks that come with relying on general-purpose AI for software development:

1. Failure to Follow Standards and Policies

AI-generated code may not comply with internal policies or development guidelines. 

2. Security Flaws

It can introduce vulnerabilities, from simple missteps like storing credentials in files to serious threats like exposing services on the internet.

3. Inefficient Resource Use

The LLM-generated code might consume unnecessary compute, storage, or bandwidth, driving up operational costs, especially in cloud environments.

4. Fragile Solutions

AI might apply short-term fixes that break existing features, introduce bugs, or even blend incompatible architectures and coding paradigms.

5. Oversimplified Code

In complex scenarios, the LLM may fail to meet requirements, miss edge cases, or even hallucinate in its responses.

6. Mediocre Results

Because LLMs are trained on public code, often from personal or low-quality projects, they may suggest solutions that are poorly written, inefficient, or insecure.

7. Overconfident Algorithms

Some models are optimized to always produce an answer, even when the right move is to pause and rethink. This behavior can clash with the experience and judgment of seasoned professionals.

Recommendations for Successful Adoption

1. Use a Development Platform

Large organizations often struggle with governance due to dispersed teams and standards. Engineering and development platforms solve this by centralizing best practices and policies, ensuring visibility and simplifying the process for development squads.

It’s also a growing trend. According to Gartner, by 2026, 80% of companies will have adopted platform engineering strategies. Integrating Gen AI into your development platform ensures that responses are not only more accurate, but also reflective of your internal standards, offering a smoother, more unified experience.

2. Educate Your Teams

AI code agents can dramatically shift a team’s dynamics. They can easily take over most of a squad’s coding duties, for example. While some teams will embrace that, others tend to resist.

These agents can function like junior or mid-level developers with three or four times the output, as if you had more “hands on deck” for your projects. Leaders must learn how to integrate them effectively into team workflows.

You should start with training and awareness. Instead of forcing adoption, track usage and results over time. Invest in AI Literacy and upskilling; after all, teams won’t adopt what they don’t understand.

3. Prioritize Contextualization

The more context AI has, the better the output. This reduces inefficiencies and ensures alignment with internal standards.

A development platform is ideal for providing centralized, up-to-date information. Techniques like RAG and Fine-Tuning take this even further.

4. Apply RAG or Fine-Tuning

To enhance the quality and relevance of your AI-generated code, it’s worth exploring techniques like Retrieval Augmented Generation (RAG) and Fine-Tuning.

RAG does not modify the model’s internal parameters (weights). Instead, it enriches the prompt with more specific and up-to-date information pulled from relevant sources. This helps the LLM generate responses that are better aligned with the project’s actual context.

Advantages of RAG:

a. It’s faster and more cost-effective since there’s no need to retrain models—just plug in the right tools.

b. It allows access to recent data, which can be retrieved and vectorized on demand.

c. It’s more transparent, making it easier to trace and debug the data used to build the final prompt.

Challenges of RAG:

a. The output depends heavily on the quality of the retrieval and vectorization (embedding) strategy. A weak setup will directly impact the quality of code generation.

b. There are limitations around prompt size. When combining the original input with RAG-enriched context, the total length may exceed the model’s context window, which can lead to truncated or incomplete results.

Fine-Tuning, on the other hand, directly updates the model’s weights by training it on a curated, domain-specific dataset. This results in a model that’s more specialized for your particular use case.

Advantages of Fine-Tuning:

a. It enables domain-specific expertise, resulting in more relevant and less generic code.

b. It produces faster responses, since the model already “knows” the domain and doesn’t require augmented prompts.

Challenges of Fine-Tuning:

a. It involves higher costs, as it requires high-quality training data and significant computational resources.

b. Updating the data isn’t simple. You’ll need to retrain the model even if only a small portion of the dataset has changed.

5. Develop Prompt Engineering Skills

LLMs are the core of generative AI, and knowing how to “talk” to these models is one of the most effective ways to improve results. It’s also fast and affordable. Better prompts lead to better answers, including more accurate and useful code.

The ability to craft clear and effective instructions is known as prompt engineering. It is a powerful and cost-effective way to boost the quality of what your AI delivers. On our blog, you’ll find recommendations and best practices for getting started on Prompt Engineering.

Use well-known and proven techniques to write clear, production-ready prompts. They can save both time and resources by reducing the number of tokens used during generation. The difference between a bad prompt and a good one might be the difference between an application that’s ready for production and one that doesn’t even compile.

6. Monitor AI-Generated Code

Last but not least, it is important to track what agents produce and how they perform. Identifying this code is straightforward, since agents can commit to repositories using designated user accounts. Reviewing their performance is also simple, thanks to Git metrics such as the number of lines changed, commits created, and PRs approved.

Separating code written by agents from code written by people helps prevent misunderstandings around intellectual property and individual contributions. It also allows teams to measure their performance more clearly, both with and without AI in the loop.

To ensure this clarity, agents should always use specific Git users, and teams should consistently track the following metrics:

• Number of commits.
• Number of lines created, changed, and deleted.
• Number of approved PRs.
• Number of bugs introduced, unresolved security vulnerabilities, and overall test quality.

At the end of the day, it should always be clear which parts of the codebase were created by humans, which by agents, and how the team’s performance has evolved with the help of generative AI.

Gen AI: The New Imperative for Development Teams

The impact of Gen AI on software development is no longer a future trend, it’s already shaping how great software gets built. Teams that embrace these accelerators are gaining speed, quality, and scale, while those that don’t risk falling behind. 

The rise of AI-powered agents, whether assistive or fully autonomous, is redefining what development teams look like and how they operate. The question is no longer whether to explore Gen AI, but how to make it work safely and effectively in your context.

Now’s the time to shift from merely “evaluating” use cases to actively reducing risks and unlocking real results.

If you haven’t started yet, hurry while there’s still time. Take control of your AI journey and lead the transformation!

References

• Impact of Generative AI in Software Development | DORA 
• Accelerate State of DevOps Report 2024 | DORA
• Is your platform a Fruit Basket or Fruit Salad? – The Architect Elevator
• What is an Internal Developer Platform (IDP)?
• What is platform engineering?
• 2024 Stack Overflow Developer Survey 
• How People Are Really Using Gen AI in 2025
• State of the CIO 2025
• Artificial Intelligence: A Modern Approach – Fourth edition – UC Berkeley
• Sequence to Sequence Learning with Neural Networks – Cornell University
• Tree of Thoughts: Deliberate Problem Solving with Large Language Models – Cornell University

To put this into perspective, LinkedIn’s 2025 Work Change Report found that 88% of C-suite leaders say accelerating AI adoption will be a priority for their businesses in the coming year.

In this article, we share some key lessons on the challenges companies face when adopting AI. For each roadblock, we offer practical tips to not only mitigate but also overcome them, helping you position AI as a driver of innovation in your organization.

Six Key Challenges in AI Adoption

According to McKinsey’s 2025 The State of AI survey, 71% of companies worldwide are already using some form of generative AI. In other words, this technology is no longer an emerging trend — it is a present reality. So, for many, addressing the core challenges in AI adoption could be what’s missing for projects to really take off.

Here’s what to watch for:

1 – Governance and Compliance

Some organizations move forward with AI adoption without clear governance frameworks in place. That opens the door to ethical and regulatory risks, including model bias and data misuse. Without well-defined guidelines and oversight, initiatives may hit legal barriers or erode trust among users and customers.

2 – Legacy System Integration

Outdated systems, siloed data, and rigid architectures often make AI integration slow and expensive. It can feel like trying to install an electric motor in a car from the 1980s: technically possible, but far from simple.

3- Costs and ROI Visibility

AI implementation demands investment in technology, talent, and time. Yet, the return on that investment is not always immediate or easy to measure. That makes it difficult for executives to justify costs, especially when benefits like, say, improved decision-making, are so intangible.

4 – Talent Scarcity

There is a shortage of professionals with experience in applied AI. And it’s not just data scientists — organizations also need leaders who understand both data and business strategy. This increases hiring costs and creates fierce competition. When experts leave, critical know-how can walk out the door with them, leaving behind a kind of loss that is hard to repair.

5 – Pilot Project Scalability 

Launching successful pilots is one thing. Expanding those projects across business units is a completely different story. Challenges with infrastructure, performance, and cultural resistance often keep AI confined to isolated experiments that never reach full-scale adoption.

6 – AI Autonomy

Determining how much decision-making authority AI should have is a constant concern. Autonomy can increase agility, but it also introduces risks. Executives worry about errors and losing control, especially when algorithms operate without proper oversight.

How to Overcome the Challenges in AI Adoption

Leveraging Governance as a Strategic Advantage

Investing early in AI governance can set your business apart. Organizations that implement policies, ethical committees, and audit mechanisms operate with greater confidence and transparency. This not only supports regulatory compliance but also builds brand credibility. That way, while others hesitate, teams running a well-governed AI framework can move forward with confidence and clarity.

Using Smart Automation as a Differentiator

Integration difficulties highlight opportunities for intelligent workflow automation. Even legacy environments can benefit from AI-powered efficiency. Rather than abandoning outdated systems, modernizing them through AI provides a competitive edge.

The productivity gains AI unlocks can also free up time and resources to support system upgrades. In other words, companies using AI to simplify their operations are simply better positioned to innovate. 

Focusing on Real-Time Decisions

Organizations often struggle to demonstrate AI’s value because they rely too heavily on conventional metrics. But the true advantage actually lies in making real-time decisions, such as reacting instantly to customer behavior or market shifts.

This agility brings measurable gains, prevents losses, and creates better customer experiences. In dynamic environments, speed is everything!

Embedding AI into the Culture

Pilot projects that fail to scale often reveal a lack of organizational integration. Instead of treating AI as a side initiative, leading companies embed it into their culture and daily operations.

Cross-functional collaboration is key. Teams from IT, data, and business units must work together on intelligent solutions. This approach fuels collective intelligence and accelerates transformation in every department, unlocking advantages that siloed competitors can’t replicate.

Democratizing Knowledge and Upskilling Teams

The scarcity of specialized talent highlights the importance of democratizing knowledge. Forward-thinking organizations document expert know-how and embed it into AI platforms. This reduces dependency on individual specialists and makes critical skills accessible to the broader team.

Training the entire team (especially non-technical employees) to use AI tools is paramount. For context, a DataCamp study has found that 69% of leaders consider AI Literacy essential for the workplace. After all, people won’t adopt what they don’t understand.

Bonus Tip: Choose Your AI Partner Wisely

Selecting the right partner is critical for a successful AI adoption. 

You should look for a provider with proven experience in either your industry or a related one, so they understand how to integrate technology without disrupting your core business. Compliance is non-negotiable, so make sure your partner follows robust standards and industry best practices.

Just as important is their ability to collaborate. The best AI partners co-create solutions, adapt tools to your strategic goals, and provide long-term support, empowering your team along the way.

Turning Roadblocks into Competitive Edge

Ultimately, your challenges with AI adoption will highlight which capabilities your organization needs to develop in order to thrive. When tackled strategically, these hurdles become opportunities to improve efficiency, intelligence, and resilience.

StackSpot AI is built to help you get there. Our integrated platform simplifies AI integration into legacy systems and smartly automates workflows.

What’s more, by offering a secure, collaborative development environment, StackSpot also enables knowledge sharing and team training, allowing companies to optimize their operations and boost innovation. 

With StackSpot, your organization is equipped to turn the challenges of AI adoption into lasting competitive advantages.

References

• Introducing the State of Data & AI Literacy Report 2025 | DataCamp
• Work Change Report | LinkedIn 
• The State of AI 2025 | McKinsey

In this article, I share my first impressions of the StackSpot AI freemium account and highlight the agents that proved most valuable in my daily work with technology.

Step-by-step: How to Create your StackSpot AI Freemium Account

When visiting the StackSpot website and selecting Enter > Login AI, you see three login options: Google, Microsoft, and GitHub. In addition to these, you can also authenticate via IDE or though Single Sign-On (SSO).

Next, you need to accept the Terms and Conditions of Use as well as StackSpot’s Privacy Policy. Simply check the confirmation box and you’re good to go — this is a standard process widely used across digital platforms.

After that, the platform asks for a few details, such as your name and position, so it can better tailor the experience and support your journey.

Once you complete this step, you’re directed to a short YouTube video with essential information on how to get started. The video is clear, concise, and provides a short overview of StackSpot AI’s key features and how it can support your work. You can watch it here:

And that’s it! In just a few steps, I was fully onboarded to StackSpot AI, with access to a variety of agents ready for immediate use.

If you would like to explore more detailed instructions on setting up your StackSpot AI freemium account, be sure to visit the platform’s official documentation.

What You Need to Know About the StackSpot AI Freemium Account

The StackSpot AI freemium account offers a fantastic opportunity to explore the platform’s capabilities. Not only does it provide access to essential features, but it also allows you to experience the power of AI in real-world development scenarios.

With the freemium account, you can take advantage of the following resources:

AI Agents: Automated systems powered by AI that serve as virtual experts. These agents analyze prior information to execute tasks, make decisions, and deliver effective solutions.

Knowledge Sources (KSs): Contextual databases that enhance AI responses by providing relevant and personalized information.

Quick Commands (QCs): Predefined instructions designed to automate specific tasks and actions quickly.

The StackSpot AI freemium account also comes with certain use limits and restrictions. Every month, you will have access to:

1 million LLM Tokens, which are used for text generation and interactions with language models.

5 million Embedding Tokens, which convert text into vectors to enable searches and semantic analyses.

If you create your freemium account using a coupon (via a personalized link), your LLM Token limit doubles to 2 million tokens.

It is important to note that this freemium access does not include features from the StackSpot EDP.

Meet the AI Agents Available on StackSpot AI

StackSpot AI features more than 10 specialized agents, each designed to perform distinct tasks. Here are some of the ones that stood out the most during my experience:

Code Explainer: This agent specializes in interpreting source code and technical documentation. Whether it is a single function or an entire project, it can analyze code with the support of Knowledge Sources.

Code Reviewer: A highly efficient agent for code review. It identifies potential bugs, suggests improvements, and recommends best practices and design patterns.

Persona Mapper: This agent helps identify and describe ideal Persona profiles for products, services, or solutions, which makes it a great tool for product development and marketing teams.

PD – UX Researcher: A user experience specialist that organizes, analyzes, and transforms research data into actionable insights.

PD – WCAG 2.2: This agent focuses on digital accessibility in compliance with the Web Content Accessibility Guidelines (WCAG 2.2). It offers detailed guidance and practical examples to help make websites, blogs, and internal systems accessible.

My Take on the StackSpot AI Freemium Account

Based on my first experience with the StackSpot AI freemium account, it is clear that the platform lowers the barrier to accessing and experimenting with advanced AI solutions for software development.

The onboarding process is not only simple but also intuitive, allowing anyone involved in the software development lifecycle—not just developers—to quickly explore the platform and experience the benefits of working with AI agents.

What stands out most is how diverse and specialized these agents are. Each one of them is purpose-built to support a different stage of the development journey, from code analysis and review to user experience research and digital accessibility.

The freemium account is, without a doubt, an excellent opportunity for tech professionals to learn, experiment, and experience firsthand how AI agents can optimize their daily work. I highly encourage you to create your freemium account and start exploring everything StackSpot AI has to offer!

At Zup, StackSpot AI has become one such transformative tool, especially for those working in complex or high-stakes product environments.

In this article, you’ll learn how StackSpot AI has enhanced my work as a product designer — bringing speed, clarity, and depth to solution development — and how this AI is redefining what it means to be a designer in the age of intelligent systems.

What is StackSpot AI?

StackSpot AI is a multi-agent platform designed for the software development life cycle. Its flexibility allows it to adapt to the specific needs of different business domains and teams.

For product design, StackSpot AI has become a powerful partner. It offers far more than development support, helping teams to structure workflows, organize knowledge, generate relevant insights, and even improve communication across the organization.

AI for Design: How StackSpot AI Transformed My Workflow

In the project I’m currently involved in, StackSpot AI helped me automate tasks that used to demand excessive time and effort. More importantly, it introduced intelligence into the design process, allowing us to deliver more efficient and user-centered solutions.

Here’s what we did and the impact we experienced.

1 – Custom AI agents

I created personalized AI agents tailored to the product design team’s needs. These agents were trained using contextualized Knowledge Sources, which made sure the solutions they generated were relevant to the specific challenges we face.

Specialist Designer Agent

The Specialist Designer Agent was developed to support product design within a specialized field. It was configured to interpret data, understand our domain, and help generate market-aligned solutions.

Not only did the agent provide valuable recommendations based on industry trends and user behaviors, but it also helped us structure processes, organize product information, and streamline communication with stakeholders. Integrated into a curated knowledge base, it has become a key enabler of effective and context-aware product decisions.

2 – Specialized Knowledge Source

To support the agent, I developed a robust and targeted knowledge base focused on sector-specific product information. Centralizing this data brought a major benefit: faster access to reliable insights with greater confidence.

Before this breakthrough, I had to search across scattered systems, manually validate sources, and cross-check versions. Now, with everything unified, I can trace sources, manage access, and dramatically reduce errors in key product design decisions while saving valuable time.

3 – Quick Command for UX Writing

To streamline writing tasks, I created a Quick Command (QC) for UX writing that was customized to our unit. This command ensured clearer, consistent, and brand-aligned communication, allowing us to deliver better content, faster. Instead of revising everything manually, I now rely on StackSpot AI to evaluate tone, voice, and clarity, speeding up delivery and boosting accuracy.

The Quick Command was particularly helpful in aligning our output with customer voice and expectations while keeping production agile and reliable.

Results 

Integrating StackSpot AI into our workflow brought measurable outcomes beyond simple automation. Check out the highlights:

• Greater process agility: We drastically reduced the time spent on operational tasks, such as searching for data and reviewing copy. This gave the team more bandwidth to focus on strategic design.

• Shift from operations to strategy: With repetitive tasks delegated to AI, we were free to dive deeper into planning, data analysis, and user-centric innovation.

• Better insights, greater accuracy: StackSpot AI helped surface patterns and behaviors that had previously gone unnoticed, improving our decisions and product direction. 

What Changed for Me with StackSpot AI

For me, the most valuable outcome of using StackSpot AI was not just improved efficiency, but rather clarity.

With fewer manual tasks, I could redirect my energy toward what truly matters: analyzing data, building strategies, and making design decisions that create impact.

StackSpot AI didn’t replace me as a designer, it amplified my thinking, sharpened my insights, and elevated the quality of my work.

Tips for Designers Exploring StackSpot AI

If you’re considering adopting StackSpot AI or other designer AI tools, here’s a short list of things to keep in mind:

1. Understand your needs: It’s important to identify your daily design challenges before implementing any new solution. This helps you set up the platform to address what matters most.
2. Leverage customization: StackSpot AI lets you build custom agents and commands. Take full advantage of this flexibility to align the tool with your design workflow.
3. Demonstrate value to your team: Tools only work when teams trust them. Share results, co-create solutions, and bring others into the process to build confidence and buy-in.
4. Track and adapt: Measure the platform’s impact and stay open to refinement. StackSpot AI evolves with your input—and so should your approach.

AI for Design: Greater Speed, Greater Depth

Embracing AI for design through StackSpot AI has transformed my work as a product designer. It’s helped me move from execution to strategy, acting faster while thinking more deeply about users and outcomes. Most importantly, it gave me back time—time to focus on making better decisions and delivering more accurate, impactful solutions.

If you also believe in technology’s power to elevate creative work, I encourage you to explore StackSpot AI and other AI design platforms. After all, the future of our craft lies in leveraging tools that scale our creativity, intelligence, and value.

Want to start now for free? Just log in with your Google or GitHub account!

Did you enjoy this post? Share your thoughts in the comments and let us know how AI is changing your approach to product design.

In this particular case, the team was challenged to raise test coverage from 0% to 80% in a highly complex legacy system. By adopting StackSpot AI and Jupyter Notebook, the team achieved:

• A reduction in unit test creation time from two hours to just three minutes
• Test coverage of up to 85%, exceeding the original target.

This synergy not only accelerated development but also improved the application’s overall quality and reliability. Read on to see how it was done. 

How StackSpot AI Gives Development Teams Their Time Back

As artificial intelligence continues to evolve, StackSpot AI stands out as an essential tool for optimizing workflows, enhancing efficiency, and reducing development time.

In recent years, AI has emerged as one of the most impactful technology trends, reshaping everything from automation to the analysis of massive data sets. Within that landscape, StackSpot AI delivers intelligent, personalized support that fits seamlessly into the developer workflow.

The platform offers extensions for all major IDEs, providing contextual suggestions, code examples, and solutions for common development challenges. As a result, developers not only work faster but also produce higher-quality code. Additionally, StackSpot AI is designed to learn and improve continuously, becoming increasingly effective with use.

Quick Command: Streamlining Development Through Automation

Creating a Quick Command (QC) in StackSpot AI is a strategic way to automate repetitive tasks and simplify workflows. Before initiating development, clearly defining the purpose of your Quick Command is paramount.

Quick Commands offer several key advantages. Not only do they minimize manual effort and reduce human error, but also bring consistency to processes, ensuring uniform execution across teams. 

Efficiency is another significant benefit, seeing as developers can complete complex tasks with a single command, freeing up time for higher-value work.

However, before a QC is released for general use, it is essential to validate its performance. This is a crucial step to avoid potential issues with the command, ensuring it is both accurate and reliable.

Case Study: Automated Testing in Legacy .Net with StackSpot AI

Modernizing legacy systems with more than 200,000 lines of code presents a formidable challenge, especially when it comes to implementing unit tests.

To address this issue, the team turned to a powerful toolset that combined StackSpot AI, especially its Remote Quick Command (RQC) feature, with Jupyter Notebook. Together, these tools streamlined prompt curation and automated testing.

Let’s dive into the details of how automated tests in legacy .Net systems became a reality with StackSpot AI.

The Legacy Challenge

The primary challenge was to achieve 80% test coverage in a short timeframe on an application that previously had no coverage whatsoever.

This highly complex application comprises more than 450 services, including controllers, diverse transactions, and integrations with mainframes (via connectors like as CICs and IMS), telephony, and media bar systems. Over more than a decade, the codebase had grown to exceed 200,000 lines.

Given its scale and complexity, initial estimates suggested that the testing effort would take over 11 months of continuous work.

Phase One: Adopting the AI-Driven Solution

To overcome this challenge, the team implemented a suite of StackSpot AI features—including Remote Quick Command, Quick Command, and Knowledge Sources—alongside Jupyter Notebook.

Using Jupyter Notebook scripts, the team extracted source files from the legacy system via StackSpot AI’s Quick Command and Knowledge Sources. Unit tests were then automatically generated based on these files.

StackSpot AI: Analyzing and Standardizing Legacy Code

StackSpot AI was critical in analyzing and standardizing the legacy .Net codebase, enabling the generation of unit tests from predefined patterns stored in the Knowledge Source.

This approach addressed the repetitive nature of the code, reduced manual workload, and ensured consistency. Prompt curation played a central role in this process, while continuous refinement helped make the tests increasingly accurate.

Remote Quick Command (RQC): Scaling Test Execution

StackSpot AI’s Remote Quick Command feature enhanced the workflow by enabling the automated submission of code files to execute the generated tests remotely.

This allowed the team to run a large volume of tests efficiently, with results being returned quickly for immediate refinement of both the scripts and the prompts.

Sending code batches via RQC also ensured tests ran in controlled environments, significantly improving the results’ reliability.

Jupyter Notebook: Orchestrating Prompt Curation

Jupyter Notebook served as the orchestration layer for prompt curation. Its interactive interface allowed developers to continuously adjust prompts and execute code in modular cells, promoting a clean and organized workflow.

This modularity made it possible to break the legacy code into more manageable parts, optimizing the test-generation process.

Setting Up Your Environment

To replicate this solution, a few steps are essential:

Installing Python

Before starting, make sure Python is installed on your system. Following that, verify that Python is working correctly by running the python –version command in Terminal.

Setting Up your Virtual Environment 

Create a virtual environment to ensure an isolated and organized workspace. This will allow you to manage different dependencies and package versions without affecting the global setup.

Once it’s been set up, the name of the environment will be displayed in the terminal, indicating it is active. You’ll also be able switch between different environments as needed.

Installing Jupyter Notebook 

Once you’ve set up your virtual environment, it’s time to install Jupyter Notebook, which you can do within the new environment using pip. For more information about Jupyter Notebook, check out their website.

This will work as an orchestration tool to help you execute cells and organize your code.

Integrating with VSCode 

To use Jupyter Notebook in VSCode, you’ll need to install specific extensions like Jupyter, which allows you to execute cells directly from the editor, and Python, which offers support for the Python kernel and integration with virtual environments.

Having set up the kernel to use your virtual environment, you will be able to run and debug cells straight from the editor, leveraging their advanced integration and automation features.

Delivering Results: A Powerful Workflow for Legacy Testing 

Combining Jupyter Notebooks with StackSpot AI’s RQC feature brought multiple benefits to automating testing in legacy .Net systems, including interactivity, integrated documentation, and data visualization.

Interactivity

• Benefit: Developers were able to experiment and iterate on code in real time. This is especially useful when adjusting prompts or testing different approaches to interact with StackSpot AI.
• How It Supports Prompt Curation: By executing code cells individually, developers can adapt and test prompts on the go, with immediate LLM feedback. This enables a more effective curation of Quick Command prompts, meaning you can tailor them to your project’s specific needs.

Integrated Documentation

• Benefit: The ability to blend code, text, and visuals in a single document makes Jupyter Notebooks a powerful documentation tool.
• How It Supports Prompt Curation: By documenting their prompt engineering process within the notebook, developers create a clear record of their chain of decisions. This helps not only with prompt curation but also with transferring knowledge to other team members and creating a source of reference for future project iterations.

Data Visualization

• Benefit: Visual tools are crucial to understanding how LLMs process and respond to prompts.
• How It Supports Prompt Curation: Developers can use notebook visualization libraries to track prompt performance, spot patterns, and identify areas for improvement. This enables more informed, data-driven prompt refinement and better results with the LLM.

Phase One Results

The synergy between these tools led to a significantly higher unit test coverage for a legacy application. Prompt refinement in Jupyter Notebook, paired with the automation enabled by RQC and the analysis capabilities of StackSpot AI, produced a streamlined, high-impact workflow.

This technical approach proved that the thoughtful integration of tools can not only accelerate development, but also enhance test quality and reduce the time required to modernize legacy systems.

This case study in implementing automated testing in legacy .Net Framework environments yielded both quantitative and qualitative results.

Quantitative Results

• Unit test creation time dropped from two hours to three minutes.
• Test coverage reached up to 85%.

Qualitative Results

• Reduced cognitive load (developers completed tasks with less effort).
• Faster development of unit tests in legacy systems.
• Improved application quality and reliability due to broader test coverage.

Phase Two: Enhanced Observability and Data-Driven Testing

In the second phase, the project focused on boosting coverage by leveraging observability data from Splunk. Logs from development and testing environments were used to generate realistic mocks and structure representative datasets.

This phase aimed to increase intelligence and autonomy but also introduced complexity, especially around tasks requiring data manipulation.

Extending Automation Beyond the LLM

While LLMs excel at generating and curating code, they fall short when handling advanced conditional logic, data transformation, or mathematical calculations.

To fill this gap, the team used Remote Quick Command to run external Python scripts in controlled environments. These scripts organized, transformed, and prepared the data for tests, supplementing the LLM. On top of that, libraries like Pandas and Numpy enabled advanced data processing, supporting everything from cleansing to transformation and mock generation. As a result, even intricate transactional data could be extracted from Splunk logs for high-fidelity test simulations.

Phase Two Results

The outcome of this more sophisticated approach was remarkable, leading to a unit test coverage of up to 94%. However, both the complexity involved in and the time required for executing the tests increased.

In contrast, the average coverage achieved using prompt-curated tests alone was around 75%, with significantly less cognitive load and post-processing.

Key Learnings from the Process

Both approaches used observability data, and prompt-based tests required minor corrections.

The main issues included non-existent code references and occasional hallucinations. There were also records of unfinished test structures, such as missing brackets or improperly closed classes, and merged test cases that broke formatting expectations.

Still, the solution delivered not only exceptional test coverage and quality, but also invaluable hands-on experience with generative AI and prompt engineering.

StackSpot AI proved intuitive and powerful, accelerating the process of creating Remote Quick Commands and integrating them into modernization workflows.

Modernizing Legacy Systems with AI: More Quality in Less Time

Through the innovative combination of StackSpot AI and Jupyter Notebook, the team successfully addressed the inherent complexity of modernizing legacy .Net systems.

This approach did more than reduce development time: it raised test coverage dramatically, ensuring greater confidence in both the process and the end product.

The results validate the effectiveness of advanced technologies in driving digital transformation, especially in systems once considered too complex to modernize efficiently.

You too can build custom solutions using StackSpot AI. Start right now by signing in with your Google or GitHub account.

Now, we want to hear from you. What did you think of this case study on automated testing in legacy .Net systems with StackSpot AI? Share your thoughts and questions in the comments below.

Content produced by Estevan Louzada Souza and Edson Massao

Today, LLMs are almost as ubiquitous as commodities, with a wide array of options — both open-source and proprietary — available for sophisticated natural language processing. But here’s the catch: the real differentiator isn’t just the choice of LLM, but how you orchestrate the entire ecosystem of models, agents, security, and governance.

The evolution of LLMs

Tech giants like OpenAI, Google, Microsoft, and Anthropic have made their Large Language Models accessible through well-documented APIs. Meanwhile, the open source community has been bustling with initiatives like the LLaMA family of models and derivatives on Hugging Face. This explosion of options has led to greater diversity and competitiveness in terms of processing power, speed, and accuracy.

According to a Vertesia report, 90% of senior tech professionals believe LLMs would bring significant value to their organizations.

However, while generic LLMs are great for broad tasks, they might not always be the best fit for specific challenges. Industries like healthcare, telecommunications, and financial services (FSI) face stricter requirements—think encryption in transit, anonymization of sensitive data, content classification, prompt sanitization, and placeholder management. This is where a mix of solutions comes into play.

Multi-LLM and SLM Orchestration

Not all LLMs are created equal. Some excel in certain areas while others fall short. That’s where Small Language Models (SLMs) come in. These models are trained for specific domains, like law and medicine, and can handle niche tasks—like summarizing patient records—with ease.

SLMs are leaner and faster, often with millions or a few billion parameters, compared to the hundreds of billions (or even trillions) in larger models. So, why settle for one generic LLM when you can tap into a suite of specialized models, each tailored for a specific need?

This is where platforms like StackSpot AI shine. Instead of replacing LLMs, StackSpot AI integrates them with SLMs, combining their strengths in a unified, intelligent way. The platform acts as an orchestrator, dynamically selecting the best model for each subtask—all while ensuring security and compliance with regulations like LGPD.

The Importance of Solid Security and Governance Processes for Orchestrating LLMs and SLMs

AI adoption isn’t just about capabilities — it’s also about trust. Data security and vulnerability management are non-negotiable. Every LLM and SLM must be reliable to prevent leaks or breaches. Here’s what you need to consider:

• Encryption in transit and at rest: Protect critical information during transmission and storage.
• Anonymization and pseudonymization: Mask sensitive data, exposing only what’s necessary.
• Data classification: Label and categorize information based on confidentiality levels, applying appropriate access policies.
• Prompt sanitization: Prevent malformed or malicious prompts from leaking private data or causing unwanted model behavior.
• Smart placeholders: Replace sensitive terms with controlled placeholders, maintaining context without compromising security.

A robust governance system is equally crucial. It should manage logs, authorizations, model versions, and data, especially in complex scenarios like those in banking or healthcare.

Governance and LLMs

Beyond integration and security, governance ensures traceability, granular access controls, and compliance with industry-specific regulations. Again, this is particularly critical in sectors like finance and healthcare, where AI adoption is tightly regulated.

The challenge is significant, but so is the payoff. A well-governed AI system can unlock new levels of efficiency while keeping risks in check.

Multiagents: Virtual Specialists

Another exciting trend is the rise of multiagent systems (AI multiagents). These platforms allow you to create multiple AI agents, each specialized in a specific task—like optimizing code, sorting data, or analyzing legal documents.

Instead of relying on a single LLM to handle everything, multiagent systems distribute tasks to the most suitable models. This approach ensures optimal efficiency for each use case.

According to Gartner, by 2028, at least 15% of decisions will be made by AI agents. Today, the number is 0%.

With StackSpot AI Multiagents, businesses can orchestrate these agents in a way that’s tailored for their specific needs. The platform offers detailed logs, permission checks, and multiple layers of security, making it a flexible and scalable solution for large enterprises.

Want to see it in action? Check out this video on our YouTube channel:

Conclusion

LLMs and SLMs are becoming more abundant and accessible. But the real game-changer lies in orchestration — how you integrate, manage, and protect these models to create a competitive edge.

In the future, we’ll see even more initiatives tackling the complexity of using multiple models. The winners will be those with the expertise to deliver reliable orchestration, ensuring quality security, and governance.

Orchestration: The Ultimate Differentiator

At StackSpot AI, we believe in the power of intelligent orchestration. Our platform, developed by Zup Innovation, empowers businesses to harness the full potential of LLMs and SLMs without being tied to a single model or compromising on security.

For companies in regulated sectors, the choice of AI models goes beyond the latest trend. The future will be a mix of LLMs and SLMs, integrated seamlessly and transparently, with robust security and governance.

So, why limit yourself to one LLM when you can have a repertoire of specialized models at your fingertips?

The decision between open source or proprietary models has taken center stage, particularly after the buzz sparked by Chinese LLM DeepSeek. This article explores the nuances of each approach, highlighting often-overlooked factors that can influence your strategy.

What are LLMs?

Large Language Models (LLMs) are complex artificial intelligence systems based on deep neural networks. Trained on massive datasets, these models learn linguistic patterns, which enables them to generate text, answer questions, and write code with impressive fluency.

They rely on transformer architecture, which processes information in parallel and captures intricate contextual relationships across text.

Open Source or Proprietary: What Sets LLMs Apart?

The fundamental distinction between open source and proprietary models lies in their level of transparency.

Open source LLMs give users access to the model’s underlying code. This openness provides the freedom to audit, adapt, and fine-tune the LLM for specific use cases. Popular open source options include Llama, MPT, Falcon, Qwen, Mistral, Yi, Mixtral, Phi-2, DeciLM, and most recently, DeepSeek.

Proprietary LLMs, by contrast, are developed by private organizations that restrict access to source code. These models often offer convenience, enterprise-grade support, and frequent updates — though they come with limitations in customization. Examples of proprietary LLMs include ChatGPT, BARD, PaLM 2, Claude 2, Grok, and Gemini.

The diagram below highlights both types of models by release timeline and parameter count:

Caption: Leading open source and proprietary models currently on the market. Source: DeepLearning.ai

But the difference goes beyond just code access. Let’s explore what makes open source LLMs uniquely adaptable — and when each model type might be the right fit.

What’s Included in an Open Source Model?

Open source LLMs offer a high degree of visibility and flexibility, especially when it comes to personalization and optimization. Their core components typically include:

Complete Source Code

Everything from model architecture to optimization logic can be accessed, enabling teams to study and modify the model’s structure. This provides a detailed view of how the LLM is built and how it interprets data.

Training Data (Where Available)

Some projects share their datasets or document the training process in detail, allowing for quality control, bias assessment, and reproducibility. However, this is rare due to data privacy and volume constraints.

Model Weights

These are the numeric parameters the model adjusts during training. Weights define how the LLM understands relationships and patterns in the data set. Each one of them encodes the strength of connections within the neural network and is crucial to the model’s performance.

Detailed Architecture

Covers model depth (number of layers), embedding size, and hyperparameters. This enables reproducibility and performance fine-tuning.

Training Process

Includes details on computational resources (including hardware), optimization methods, and training metrics — crucial for replicating or improving the model.

Algorithms and Techniques

Information about how the model handles input, augments data, and applies regularization is vital for practical use and innovation. 

Community and Documentation

Strong communities often support open source efforts, offering insights, fixes, and plugins. What’s more, comprehensive, accessible technical documentation accelerates onboarding and deployment of the model.

Key Differences Between Open Source and Proprietary LLMs

To choose the right model, one must consider more than source code. Trust, flexibility, cost, and support all weigh into this decision. Let’s break down how the two approaches compare across these four critical dimensions:

1. Transparency and Trust

• Open Source: Allows audits and in-depth analysis, making it a strong fit for teams that require oversight and explainability. 
• Proprietary: Operates like a black box. While reliable, its lack of transparency can hinder accountability in regulated industries. 

2. Personalization and Customization

• Open Source: Easier to tailor to domain-specific tasks. It is especially useful in fields like healthcare, law, and engineering, where terminology is domain-specific.
• Proprietary: Prioritizes ease of use but restricts how much the underlying system can be altered—updates and feature sets are controlled by the vendor.

3. Costs and Resources

• Open Source: While licensing is free, deploying these models requires significant infrastructure and technical expertise.
• Proprietary: Typically involves usage fees (e.g., token-based pricing) but bundles in support, scalability, and system reliability.

4. Support and Security

• Open Source: Community-driven support can be robust, but lacks service-level agreements (SLAs).
• Proprietary: Includes guaranteed support, regular security patches, and enterprise-grade SLAs.

Choosing the Right LLM for Your Business

The strategic decision between an open source model and a proprietary model can directly impact your results. However, there’s no one-size-fits-all answer. Each option has its place depending on business goals, technical maturity, and regulatory demands. With that in mind, let’s take a look at how you can determine which path best suits your use case.

When Open Source Makes Sense

• Research and Innovation: Ideal for universities, startups, and R&D teams that need full control to experiment.
• Specialized Applications: When tasks demand niche vocabularies or workflows, the ability to customize is essential.

When Proprietary is the Better Fit

• Mission-Critical Systems: For enterprises that cannot afford downtime or require rigorous compliance.
• Plug-and-Play Integration: Proprietary models often integrate more easily with enterprise platforms, saving time and money.

A Hybrid Model May Offer the Best of Both Worlds

Choosing between open and closed LLM architectures doesn’t have to be binary. Many companies are pursuing hybrid strategies that combine the innovation and transparency of open source models with the robustness and support of proprietary models. This strategy can offer businesses the ideal balance between personalization and reliability.

Hybrid systems often orchestrate multiple LLMs alongside smaller models — known as Small Language Models (SLMs), designed for edge computing, cost optimization, or narrow use cases.

In a highly competitive landscape, the ability to integrate and manage various models can turn AI into a long-term competitive advantage. According to CB Insights, 94% of surveyed companies now use more than one provider in their LLM stack—citing benefits like cost efficiency, specialization, and reduced vendor lock-in.

StackSpot AI: A Flexible LLM Platform

StackSpot AI defaults to OpenAI’s ChatGPT models, but it doesn’t stop there. It offers the flexibility to integrate a variety of other models — both open source or  proprietary — within the same environment.

The platform supports intelligent orchestration across multiple models (both LLM and SLM) and allows AI agents to collaborate using different underlying systems. This enables tailored experiences for each use case or department.

Conclusion

Whether you opt for open source transparency or proprietary convenience, choosing the right language model requires strategic consideration. Your final decision should align with your business goals, technical maturity, and compliance landscape.

Considering elements such as cost, security, and support can help you determine which model best meets your demands. Ultimately, a hybrid strategy may offer the optimal path forward — leveraging the strengths of both ecosystems while minimizing their limitations.

Now we’d like to hear from you. What type of model are you considering for your organization? Share your thoughts or questions in the comments!

References

• 10 LLMs (Large Language Models) Open-Source Para Uso Comercial – Data Science Academy
• 8 principais LLMs de código aberto para 2024 e seus usos | DataCamp
• Uma introdução aos Large Language Models | by Nelson Frugeri, Jr | Medium
• How Transformer LLMs Work – DeepLearning.AI
• The foundation model divide: Mapping the future of open vs. closed AI development – CB Insights Research
• Should enterprises adopt closed-source or open-source AI models? – CB Insights Research