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Whether you're a student juggling research papers, a professional preparing a major presentation, or a creative mind organizing a new project, the challenge is the same: how do you synthesize countless articles, videos, reports, and notes into coherent insights?

What if you had an AI assistant that only read your stuff? An expert that knows your project inside and out and can instantly answer any question you have about it.

That's NotebookLM. Built from the latest Gemini models, it’s a research and thinking partner designed to turn your digital clutter into clarity.

And before you ask: your data is private. NotebookLM does not use your documents, your questions, or its answers to train any AI models.

What is NotebookLM? 

NotebookLM is an AI research assistant that works exclusively with the documents you provide.

Here’s the key difference: unlike general-purpose AI tools that pull answers from the entire internet, NotebookLM becomes an expert on your information and nothing else.

The secret sauce is a technique called Retrieval-Augmented Generation (RAG). In simple terms, this means before NotebookLM answers your question, it first finds the relevant facts directly from your sources. This grounding technique is why its answers are so reliable and it doesn't "hallucinate" or make things up. 

What Can You Do With NotebookLM? A Feature Breakdown

NotebookLM is more than just a chatbot for your files. It’s a full suite of tools designed to help you understand, synthesize, and create new insights from your own material.

1. Upload (Almost) Anything

It all starts with your sources. In NotebookLM, you can upload up to 50 documents per notebook, creating a focused, private "mini-internet" for the AI to work with. By limiting the AI to only your sources, you control the context completely. 

You can upload a wide variety of formats, including:

• PDFs and Google Docs
• Website links
• YouTube video transcripts
• Google Slides
• Copied text

2. Get Answers You Can Trust (with Proof)

Once your sources are uploaded, you can start asking questions. The magic of NotebookLM is that every single answer comes with citations, linking you directly back to the exact passage in your source documents.

This is the ultimate fix for AI hallucinations. You never have to guess where an answer came from. You can instantly verify every fact, making it an incredibly reliable tool for researchers, students, and professionals who need accuracy above all else.

3. Instantly Generate Reports

NotebookLM can synthesize your source material into professionally formatted documents, saving you hours of tedious writing. With a single click, you can generate:

• Briefing Doc: A comprehensive overview of your sources, complete with key insights and summaries. Perfect for getting up to speed on a new project.
• Study Guide: An invaluable tool for learners that creates quizzes, essay topics, and a glossary of key terms from your notes.
• Blog Post: A ready-to-publish article that distills the most insightful takeaways from your research into a highly readable format.

4. Visualize Connections with a Mind Map

For visual thinkers, this feature is a game-changer. NotebookLM can automatically generate an interactive mind map that shows the relationships between key topics, people, and ideas across all your source documents.

A mind map helps you see the bigger picture. It can reveal hidden connections and spark new lines of inquiry that you might have missed by just reading the text, making it perfect for brainstorming and strategic thinking.

5. Audio & Video Explainers

Need to review your notes but can't be tied to a screen? NotebookLM can generate a custom podcast-style audio conversation or even a short, narrated video explainer based on your documents.

It can create an audio Q&A, a discussion, or a short video that walks you through the key points, complete with a voiceover. You can listen to a deep-dive on your research or watch a quick summary of your meeting notes, absorbing key information without being glued to the page.

Conclusion

NotebookLM isn't trying to be an all-knowing oracle that has an opinion on everything.

It’s a focused, private tool designed for a single purpose: to help you think better and work smarter with the information that matters most to you. By giving you reliable answers, powerful summaries, and new ways to see your own data, it acts as a true partner for your brain.

In September, we joined Nerdearla 2025, the largest free science and technology event in Latin America. For five days—September 23–24 online and September 25–27 at Ciudad Cultural Konex in Buenos Aires—over 10,000 in-person attendees and 40,000 online participants explored the future of technology, from artificial intelligence to user experience.

What is Nerdearla and Why It Matters for the Tech Community?

Nerdearla was born with the mission of promoting learning and collaboration within the global tech and open-source community. The event mixes talks, workshops, and networking spaces, with free streaming worldwide and in-person activities.

The 2025 edition featured multiple tracks: Development, Data Science, Product, Infrastructure, Security, AI, Testing, UX, and Soft Skills. This makes it an important event for professionals looking for emerging  trends, inspiration, and community connections.

How We Got There: A Team Initiative

Our participation in Nerdearla 2025 started as an internal proposal from the UX team. After reviewing the idea with company leaders, the delegation was shaped into a group of two designers and one developer, a mix designed to maximize value and capture insights from different perspectives.

This ensured the experience was valuable for each of us and for the team as a whole, aligning with both our company values and our professional growth goals.

Top Nerdearla 2025 Highlights

Here’s a breakdown of the key conversations that dominated the sessions we attended.

Development: The Role of Open Source and AI

• Many talks focused on the role of open source  (software with publicly available code that anyone can use, modify, and share) and how to integrate it into real projects.
• Emerging technologies, especially AI, were discussed as enablers for future solutions.

Recommended talk: "Programming is dead. Long live programming!" – Miguel Ángel Durán García

Design: UX Research and Data-Driven Decisions

• The emphasis was on research and data as continuous practices, not just starting steps.
• Metrics and insights help position design as part of product discussions.
• A standout session reminded us that AI doesn’t replace design but reshapes our role. It introduced AX (Agent Experience), showing how UX evolves to guide AI agents in ways aligned with human goals:  “Now this UX is changing… and it’s not optional, welcome to the world of AX.” – Ana Rangel

Product: Applying AI with Purpose 

• Sessions stressed the importance of evolving practices in the era of AI.
• Risks of adopting AI without a clear goal: using it as a buzzword or add-on can be worse than not using it at all.
• Value comes when AI is tied to real problems, generating improvements in productivity, decision-making, and user experience.

Recommended talk: "Build the right product (before writing a single line of code). Validation, purpose, and focus."

One common takeaway was that AI should not be added just for the sake of it but rather used as a tool to solve a real, defined problem.

A Few Talks That Stayed With Us

We’d love to share a few of the sessions we kept talking about long after leaving the room:

• Data & AI Ready: the framework behind hyper-personalization – G. Cabrera & M. Trevisan
• The MCP Protocol: "More Context Please" – Guido de Caso
• How to evolve your product with AI without sacrificing quality – Nano Vazquez
• From intuition to impact: how UX Research guides our product decisions

Key Takeaways for Our Team and Clients

Some of what we heard confirmed our core beliefs: making decisions without data is like working in the dark; AI is here to stay as a tool to speed up processes and solve problems; and research should be present in every stage to build a successful product.

At the same time, we discovered new perspectives that broadened our view. What stood out most is that our company supports both individual and team growth, and Nerdearla gave us the space to grow together. That growth now flows back into the projects we deliver.

The Future of Tech: Final Thoughts from Nerdearla

Nerdearla 2025 made it clear that combining research, data, and new technologies—especially AI—will continue to shape the industry. For us, it was a moment to validate our approaches, learn from others, and bring fresh ideas back to our work.

At Kaizen, we believe building an amazing team is everyone’s job. That’s why our hiring process isn’t siloed in one department, it's a hands-on, collaborative effort where everyone from developers and designers to project leads has a voice.

The result is a team of people who truly reflect our values and are excited to grow with us. The proof is in our stability: our annual turnover rate is just 5%, and an incredible 0% among key players.

We’ve designed our process to be thorough, not bureaucratic. From start to offer, it typically takes 3 to 6 weeks. Here’s a look at how it works.

We Start on the Inside

Before we post a role publicly, we share it internally through KaizenOS, our web app built to bring transparency into our organizational structure. We’re committed to our team’s growth, so we give them the first shot, whether they’re ready for a new challenge themselves or know someone who’d be a great fit.

Only after we’ve explored internal options do our People Care team and the project lead sit down to define the role and shape a job description that reflects what we truly need.

Step 1: Screening

This is a relaxed, get-to-know-you call with our People Care team. We’ll chat about:

• Your background and work experience
  
  
• What drives you and what you’re looking for in your next role
  
  
• Your English level
  
  
• Salary expectations

But this is a two-way street. We want you to grill us, too. We’ll give you the inside scoop on Kaizen and answer any questions you have.

Step 2: Cultural Interview

This part of the process is special. It’s not run by People Care, it’s led by fellow Kaizeners from all areas of the company: developers, designers, leads, you name it.

Anyone who wants to join in can volunteer. Why? Because our culture is our people. And they’re the best judges of who’ll thrive here.

We’re looking for teammates who:

• Communicate with clarity and kindness
• Believe great ideas can come from anywhere and can challenge them respectfully.
• Take ownership and proactively look for solutions.
• Are naturally curious and always eager to learn.
• Aren't afraid to speak up or ask questions.

Step 3: Technical Interview

This is where we dive into your expertise. You’ll meet with senior team members who know the role and the tech inside and out. We skip the brain teasers and "gotcha" questions, and we focus on practical, real-world problems.

For junior developers, we start with a short (1-hour tops) take-home exercise. It’s not a pass/fail test; it’s a starting point for a conversation about how you think, how you solve problems, and how you approach the work. 

Step 4: Team Meeting

In this stage, you'll meet with your potential teammates for a practical conversation about the project's challenges and to assess the mutual fit.

Then, for client-facing roles, there is an optional instance with the client. Think of it as a chemistry check to make sure it’s a great fit for you, the client, and us. 

To keep things fair and focused on skills, we often use a "blind" version of your resume for this stage. We remove details like your name and past employers so the focus remains 100% on your skills, experience, and talent. It’s a simple way to reduce bias and let your work speak for itself.

Step 5: Psychotechnical Interview

The final step is a one-on-one psychotechnical interview with a People Care specialist. Through a conversation that includes some competency-based questions, we get to know you on a deeper level.

We believe in a holistic view, so this chat isn't a make-or-break step; it's a valuable part of the overall evaluation. The information gives us insight into how you might behave in certain scenarios and, most importantly, helps us prepare the best possible onboarding for you, tailored to your needs.

Sound Like Your Kind of Team?

This process is how we build the dedicated, invested teams that our clients love. It’s how we find people who don’t just work at Kaizen, but actively shape it.

If this way of working feels like a good fit, we’d love to hear from you.
👉 Explore Careers

And if you’re a company looking for a team that brings this same care, collaboration, and energy to every project, we’re here for that too.
👉 Let’s Discuss Your Project

What’s the hidden cost of high team turnover in software development? It’s not just missed deadlines or mounting frustration. It’s the constant re-explaining of your business. The knowledge that quietly walks out the door. And the sinking feeling that you're managing vendors, not building with partners.

This is all too common in outsourcing, where the relationship rarely goes beyond being just an “extra pair of hands.” But what’s possible when a collaboration is built to last?

This is the story of one such partnership. A journey that began in 2016 with a single developer and grew into a fully integrated team of 30. It shows what happens when a relationship is built on stability, shared ownership, and mutual trust.

The Foundation

Our journey with our client started with a clear goal: to merge multiple legacy systems into a single modern web platform that facilitates electronic customs and border entry filings for the US and Canada.

Our involvement began with just one developer joining our client’s team. But their first task wasn’t writing code, it was flying to our client’s headquarters. We felt it was essential to sit at the same table, to listen, and to understand the “why” before ever building the “what.” 

From Execution to Strategic Insight

In the early years, our relationship grew not just because of the quality of our code, but because of the quality of our questions.

Our philosophy is to invest heavily in our people’s well-being and growth. With a horizontal culture built on participatory decision-making, competitive salaries, and top-tier benefits, we’ve created an environment where high performers choose to build a career.

That translates directly into value for our clients. With just 5% annual turnover, and 0% among key roles, the team our clients start with is the team they grow with.

This is where the value of a stable team really comes into focus. When people stick around, they have the opportunity to compound business context, understanding not only how the system works, but why it matters. They start to see around corners, anticipating challenges and contributing to strategy, not just executing tasks.

We flagged problems in specs before they became blockers. We suggested simpler solutions that saved time and money. That’s when the relationship started to shift. It was the first sign of something deeper taking shape.

From Collaboration to Shared Ownership

The strength of our partnership was proven when our collaboration grew beyond the initial project. Our client entrusted us with four more of their development initiatives, which brought us to a pivotal moment: how to scale our team to meet this new level of responsibility.

The pivotal moment wasn't a single event, but a gradual shift in mindset. In many projects, a client dictates the workflow. But as we demonstrated our deep understanding of their goals, the conversation changed. We earned the trust to not just follow a process, but to define it. 

That level of trust unlocked a new level of collaboration, proven by the tangible responsibilities we began to co-own.

We gained the autonomy to negotiate technical scope with stakeholders and took charge of defining the development processes.

The most significant proof of this partnership emerged when two key, high-responsibility positions on the client’s team became vacant. First, the critical role of coordinating all production deployments, a position of immense operational responsibility, was assumed by one of our team leaders. This shifted our role from simply building software to owning its safe delivery.

Shortly after, when a veteran developer who handled complex custom work also departed, the critical tasks were not filled by a new hire. Instead, the client entrusted these responsibilities to our entire team. Stepping up to absorb those tasks together was the moment we truly became co-owners of the project, sharing a deep sense of accountability.

A Single Team, A Shared Identity

Eight years after that first visit, the lines between our teams have all but disappeared.

This deep connection was built on more than just good work. It grew from our commitment to face-to-face team building, organizing more trips as the team grew, and from finding common ground in the small things, like talking about the same sports, TV shows, and movies. This shared culture turned daily meetings into genuine conversations between colleagues.

The result is an integration so complete that, in the words of the team:

“We’re the same. There’s practically no difference who is who. There is no ‘us and them.’”

When you give a partnership the space to grow, you don’t just get better software, you get the stability you need to innovate and grow.

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We live in an era of accelerated transformation, where AI tools are unlocking unprecedented efficiency. However, a tool that’s a game-changer one day can become a risk the next.

How do we adopt these technologies with agility and ambition without compromising the security of our clients and our own business?

Today, we want to share the creation of an internal team of Security Advocates.

When a Core Tool Changes Security Rules

Like many, we used OpenAI's tools. But the recent fallout from the New York Times lawsuit means their data policy has fundamentally changed. The "30-day data retention” guarantee is gone.

For us, that’s not just an internal issue; it’s a direct conflict with the promises we make to our clients. Every code snippet, every piece of sensitive data passed through that API could now be stored indefinitely for legal review.

The typical corporate playbook for this is simple: a few managers lock themselves in a room, make a decision, and send out a company-wide memo.

We use a different approach.

A Human Bridge We Call "Security Advocates"

Instead of a top-down "control tower" that dictates policy, we build "bridges." For this, our bridge is the Security Advocates team.

This is a cross-functional group with a representative from every team at Kaizen, from product development, marketing, and finance to infrastructure and people care.

These advocates are facilitators and translators. Their job is a two-way street:

• They share security best practices with their teams.
• More importantly, they bring the questions, the frustrations, and the real-world needs of their daily work back to the group.

This isn't about consensus-driven committees; it's about a constant, high-speed dialogue that helps us build policies that actually work for the people who have to live with them.

Figuring Out the OpenAI Shift

This team was put to the test the same week it was formed, showing just how fast we need to adapt.

When the OpenAI news hit, our first move wasn't to issue a ban. It was to ask an open question through our Advocates: "This is happening. How does this actually impact your day-to-day work?"

The goal was to get an honest, ground-level picture:

• Would a developer lose a tool that’s critical to their workflow?
• Does our design and marketing team rely on GPT for creative tasks that an alternative can’t handle?
• Is there a use case somewhere we haven’t even thought of?

Initially, we presented a clear proposal: discourage the use of OpenAI where data retention is an issue and use an alternative like Claude that better aligns with our privacy goals. We were also brutally honest about the trade-offs: the alternative is safer, but it might require different prompting and adjustments.

The final outcome wasn't a total ban. After discussions with the design team and others, we landed on a hybrid approach: we’d limit OpenAI for any work involving sensitive data but still allow its use for non-sensitive, conscientious tasks. We even built in a process to use it on specific projects if a client gives us their explicit consent.

This flexibility is the direct result of having the conversation out in the open, creating a solution we all own because we all understood the 'why' and had a hand in building it.

This is Our Value

This approach is more than a security strategy; it’s our culture in action. In a world where the ground is constantly shifting, we believe the most robust and responsible solutions come from having every voice in the room. Our clients expect us to be adaptable and trustworthy, and this is how we deliver on that promise, not just by having the right answers, but by having the right process to find them.

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Large Language Models (LLMs) are powerful, but they come with two big limitations:

• They don’t always have the most up-to-date knowledge.
• They can only remember what fits in their context window.

Retrieval-Augmented Generation (RAG) solves this by connecting your LLM to an external knowledge base and retrieving relevant context before generating a response. (And yes… RAG also means “rag”, but here it’s definitely more high-tech than something you use to clean the kitchen 🧽).

1. What is RAG and how does it work?

Think of RAG as an assistant that searches first, then answers.

1. The user sends a prompt.
2. The system retrieves relevant information from a database.
3. That information is added to the original prompt.
4. The LLM generates the final answer.

Example: An HR chatbot could first retrieve the latest company policy document before answering questions about vacation days.

2. Retrieval techniques you can try

When it comes to finding the right information for your LLM, there are several approaches, each with its own strengths.

The most straightforward one is Keyword Search, where the system looks for exact or partial word matches in the text. This is simple, fast, and effective when you know the exact terminology to look for. For example, using BM25, you could pinpoint the exact paragraph in a technical manual that matches a user’s query.

Then there’s Semantic Search with Embeddings, which goes beyond exact matches to find text with the same meaning, even when the wording is different. This is particularly useful for cases like retrieving answers about “sick leave” even if the document calls it “medical absence.” By understanding synonyms and related concepts, semantic search adds a powerful layer of flexibility.

Finally, you can combine the best of both worlds with Hybrid Search. In this approach, the system runs both a keyword-based search (like BM25) and a semantic search (using embeddings) in parallel.

The results from each are then merged and re-ranked, often using techniques like Reciprocal Rank Fusion, so that documents highly ranked by either method can appear at the top.

This way, you capture exact matches for critical terms while also retrieving contextually relevant content that may be worded differently, making it especially powerful for cases like technical FAQs where precision and broader understanding both matter.

3. Improving your results

Even with a good retrieval strategy, not all results are equally useful. That’s where optimization techniques come in.

One of them is re-ranking with specialized models, such as cross-encoders. Instead of scoring the query and each document separately, cross-encoders process them together, allowing the model to understand fine-grained context and relationships between words. This produces more accurate relevance scores, ensuring the most useful documents appear first, even if they don’t share many exact keywords with the query.

Another useful approach is Metadata Filtering. By filtering results according to attributes like date, category, or document type, you can eliminate outdated or irrelevant information. Imagine narrowing your search to documents updated in the last six months – it’s a simple step that can drastically improve the quality of the information your system uses.

4. Preparing your data: the art of chunking

LLMs can’t process huge documents all at once. Chunking means splitting them into smaller pieces.

For example: Split a 100-page manual into 500-word sections with 10% overlap to ensure no important context gets lost.

Benefit: Improves retrieval relevance and accuracy.

5. Measure your performance

You can’t improve what you don’t measure.

Here’s two key metrics for RAG:

• MAP (Mean Average Precision): How well relevant documents are ranked.
• MRR (Mean Reciprocal Rank): How high the first relevant document appears.

Impact example: After optimizing, relevant documents moved from position #5 to #2, reducing search time for users.

6. What’s next for us at Kaizen?

The most exciting part is putting this knowledge into action.  

We see immediate opportunities to:

• Boost the performance of chatbots for clients and internal tools.  
• Experiment with hybrid retrieval to improve accuracy.  
• Apply chunking strategies to make better use of large document sets.

Because in the end, whether it’s a rag for cleaning or RAG for AI, it’s all about wiping away the mess and delivering sharper results. 😉

When you’re growing fast in a decentralized structure, a simple question becomes hard to answer: Who’s doing what—and where are we stretched too thin, thriving, or missing something entirely?

Those questions sparked the creation of KaizenOS, an internal web app built to bring transparency into our organizational structure.

The Challenge: When Growth Outpaces Visibility

At Kaizen, we’ve always embraced a horizontal structure. That means decisions aren’t handed down from the top, they’re shaped and driven by the people doing the work.

In practice, this means a lot of our people wear multiple hats. A front-end developer working on a client project might also be leading initiatives in the Innovation Hub, a space where our engineers explore ideas and experiment with new technologies to push innovation forward, both internally and for our clients.

When we were a small team, this organic way of working just worked. Everyone knew who was doing what, and it was easy to collaborate or ask for help.

But as we grew to over 80 people, things started to get blurry. Who was owning what? Where were the gaps? Who was overwhelmed, and who had space to grow?

Our way of working hadn’t changed, but the scale had, and with that, we started losing visibility.

The Vision: A Tool to Enhance Transparency

The Innovation Hub took on the challenge. Our goal was to support how we already work, with a solution that makes things visible and easy to navigate for our team.

We imagined a tool that could:

• Map how our teams are structured
• Show current roles and active responsibilities
• Identify roles that are open or about to be vacated
• Highlight opportunities for rotation or shadowing
• Flag people who might be spread too thin
• Create context for people to grow into new roles

The Experiment: An AI-Assisted MVP

We didn’t want this to be a long project. We wanted to build fast, validate fast, and learn fast.

So we ran it like an internal experiment, using AI tools to speed things up. We set a clear constraint: no more than one person-month of total effort.

Here’s how we broke it down:

• ½ person-month – AI-assisted Developer
• ¼ person-month – AI-assisted UI/Designer
  ¼ person-month – AI-assisted Product Owner

We used Windsurf, an AI-powered IDE, to help us ship quickly. The goal was to build a working MVP in under 180 hours, and we made it.

This experiment also became about exploring what AI-assisted development can look like in practice. Could we reduce build time and cost while still producing something valuable? The answer was yes.

KaizenOS: Our Custom Operating System

The result is KaizenOS, a web app that gives everyone at Kaizen a clear, real-time view into how responsibilities are distributed across the company. The teams themselves created the maps and roles (you only have to be a Kaizen member to modify them), which enabled conversations to clarify who does what.

And we’ve already started publishing internal vacancies.

KaizenOS supports our belief that scaling a company doesn’t have to mean adding more control. You just need visibility, trust, and a structure that enables people to step up and proactively propose solutions.

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I just finished a course that I have to tell you about, especially if you’re building with LLMs. You know the drill, you build something cool, but the model starts making things up (“hallucinating”) or its knowledge is stuck in 2023.

We’ve all been there. But there’s a practical fix called Retrieval Augmented Generation (RAG), and the team at DeepLearning.ai has released a course to master it, and it’s 100% free to learn.

What is RAG, and Why Should You Actually Care?

LLMs are incredible, but they come with well-known limitations. They can make up facts, and their knowledge is limited to their training data (which is always out of date).

RAG is the elegant solution to these problems.

Instead of just using the LLM’s static knowledge, a RAG system first retrieves relevant, up-to-date information from a reliable source, like your company’s wiki, a live database, or any other source you trust.  It then augments the prompt with this information, giving the LLM the exact context it needs to give a smart, accurate answer.

Long story short: it’s how you give context to an LLM to stop making stuff up and start using real, up-to-date information.

So, What’s This DeepLearning.AI Course All About?

This course it’s built to take you from “What’s RAG?” to build production-ready applications. The knowledge is completely free. If you want a certificate to pop on your LinkedIn, that’ll run you about $49, but it’s totally optional.

Who is This Course For?

This is definitely for engineers who already have some Python skills under their belt. You’ll be in good shape if you have:

• Intermediate Python skills.
• A basic grasp of what generative AI is.
• High school–level math (nothing too crazy).

Here’s a Look at What You’ll Learn

The course is self-paced and broken into five chunks. They estimate it takes about a month if you put in around 5 hours a week.

Here’s a quick rundown of the good stuff:

• Build a Full RAG Pipeline: You’ll learn how to actually connect an LLM to an external knowledge base from the ground up.
• Master Modern Search: Go beyond basic searches with techniques like semantic and hybrid search, using things like vector databases.
• Data Handling: Dive into essential concepts like chunking and query parsing for optimal retrieval.
• Handle Real-World Data: Learn the essentials of chunking and parsing documents so the retrieval part actually works well.
• Evaluate and Deploy: Figure out if your RAG system is actually any good, learn how to spot hallucinations, and get it ready for prime time.

My Take: Is This RAG Course Worth Your Time?

Absolutely. RAG is quickly moving from a “nice-to-have” to a “must-have” skill for anyone serious about building practical AI applications.

What I love about this course is that it pushes you past the simple “hello world” examples and into the stuff that matters for real-world deployment, like evaluation and optimization.

For me, this is the most direct path to leveling up your AI skills and building applications that are genuinely more reliable and powerful. It’s a skill that will absolutely make you a more valuable developer.

The Quick-Hit FAQ

I figured you might have a few questions, so here are the quick answers.

Is the DeepLearning.AI RAG course really free?

Yes. All the videos and labs are totally free. You only pay (about $49) if you want the official certificate at the end.

How long does the course take to complete?

It’s self-paced, but plan for about 5 hours a week for a month to get through everything comfortably.

Do I need to be an AI expert to take it?

Not at all. As long as you’re solid with Python and know the basics of AI, you’ll be fine.

Ready to Start Building Smarter AI?

If you’re tired of the limitations of off-the-shelf LLMs, this is your next step. Stop letting your models guess and start giving them the facts.

Check out the free Retrieval Augmented Generation (RAG) course on DeepLearning.AI

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I’ve been riding the AI wave from the very start here at Kaizen Softworks, with all the hype, concerns, doubts, and broad vision that come with it.

This post is about how Kaizen Softworks went from small AI experiments to full company-wide adoption, building infrastructure, policies, and culture that allowed us to enable every developer with ChatGPT-5 within hours of its release.

Our First Steps into the AI Wave

It began small: fewer than a handful of us asking, “What exactly is AI?” We decided to dig deeper, run some experiments, laugh at the hilariously bad outputs we got from time to time, and be amazed by the little gems we discovered along the way.

Thanks to Kaizen’s horizontal structure, we could start processes and workflows in a very autonomous way, a rare experience if you’re not used to flat organizations. We created plans, ideas, and ways to move forward, and we got going quickly.

We knew we wanted to explore many areas, without knowing which ones would pay off. We set aside modest resources for things like service licenses, learning, workshops, and even reserved a chunk for a local server.

Coming from an infrastructure background, I felt responsible for creating the tools and environments that enable people to do things. While other companies were banning AI, we were promoting it, not pushing, just enabling. Some people even expressed concern that they were using AI more for personal purposes than for work, due to unclear regulations, permissions, or acceptance, or simply because they didn’t know how to apply it. Our advice was: just do it.

DonIA: Our Custom Server and Enabling Philosophy

Before long, we built our custom server: DonIA. Even the name came from a fun exchange with Nacho, Kaizen’s most hyped-up teammate. We spoke to finance for resources, to marketing to let them know our intentions, and to our CEO. No one fully understood where we were headed, honestly, neither did I, but everyone agreed it was worth supporting at full speed.

We presented what I still believe is some of the most professional work done, without specs, without deadlines, without a product manager, and by carving out time wherever we could, often making space where none existed. It was driven purely by interest and passion.

From a Risky Bet to Company-Wide AI

Just a few weeks later, Martín and Nacho had built up enough momentum to get a specific team excited about going full speed with AI for a client. It felt like a risky bet. The safer answer would have been, “No, too risky. We can’t go all in.” But Alex’s fire in his eyes told us that wasn’t an option. So we moved forward, enabling the team to use AI in a real production context: deadlines, constraints, and all.

We created security documentation, studied privacy issues, got our servers online so we could run non-cloud, on-premise versions if needed, drafted policies and workflows, and within days, the team was using AI in their workflow.

And that effort wasn’t just for one project; the frameworks were designed for the entire company. If we were going to push AI forward, we had to embrace it company-wide.

We built toy projects day after day, read forums, shared experiments, and held weekly meetings with tons of updates. Everything was shared openly for discussion.

Within a few months, we had a complete framework for almost anyone at Kaizen to make great use of AI tools. It didn’t feel fast at the time, sometimes it felt slow, but for a team with zero officially allocated hours, it was impressively steady.

Eventually, we decided to go further. We aligned with our CEO, finally allocated resources, and formed a small but dedicated team. We brought together advisors from every team in the company to gather blockers, share ideas, and make decisions for the greater good. The involvement and professionalism were incredible, it felt like everything was falling into place.

And today is the payoff. ChatGPT-5 was released just seven hours ago, and within hours (not days, not weeks, and definitely not months), we achieved something remarkable

All teams are now fully aware of the security, copyright, and privacy concerns, including, for instance, OpenAI’s lawsuit in the context of general-purpose chat tools. They also know exactly which services offer zero data retention, and which don’t.

Our infrastructure was upgraded to GPT-5 within hours of release, and we already have experiments running.

It’s 3:30am right now, tomorrow every Kaizen developer will start the day with access to state-of-the-art AI tools and the knowledge to use them effectively. From experience, I know this would normally take weeks or even months without the groundwork we’ve all put in.

Milestone Reached

Kaizen’s horizontal structure certainly helped, but it was the commitment from the people, from the hypers to the conservatives, that made this possible. Everyone added their grain of salt, some brought the whole bag, but all contributions were equally important.

I’m incredibly proud of this team. Congratulations to all, well done!

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