We're all surrounded by AI. We interact with it daily in AI chat clients like ChatGPT and Gemini, and for many of us, it's deeply embedded in our workflow with code assistants like Cursor and Junie. For a while, I was comfortable with the high-level understanding: there's an LLM, we supply it with context, and magic happens. But we've all seen the headlines about data leaks stemming from sensitive information being passed in prompts. So we know there are boundaries, but the engineer in me had a nagging curiosity: - Where, exactly, are those boundaries? - How does it all actually fit together? - What's the real architectural difference between a simple chatbot and an agent that can meaningfully write code? The "it just works" explanation was no longer enough. This course was exactly the deep-dive I was looking for. It gave me so much clarity, developing my vocabulary and painting a clear picture of the architectures around LLMs. Crucially, it also lays out key design patterns that help conceptualize how agent code can be structured for maintainability and scale. It walks you through the fundamental, boilerplate-level code, which is the best way to understand the mechanics behind those patterns. Of course, as I've been graciously informed by my junior peers, actual development of one's own agents will use powerful tools and libraries like LangChain or Google's Agent Development Kit (ADK) to accelerate the process. I found that understanding the "boilerplate" and design patterns first makes the power of those higher-level tools even clearer. If you want to get under the hood and build that foundational knowledge, I highly recommend this course. It connects the theory to the practical, which is where real innovation happens.

I become the fan of Jules White after taking this course. Sir you taught me not only the knowledge but also the thought process. This was an incredible learning. Thanks

Excelente buen tema

Very clear. I dont rate it 5 stars because it is 100% oriented to java devs. I work in .net