How I AI #0: Why This Series Exists

I didn’t start learning AI to train models. I started because I wanted to understand how to build reliable AI systems.

After spending more than a decade building distributed systems and backend platforms, I was curious about what actually happens behind the demos. How do RAG systems work? Why do agents fail? What makes one AI application feel reliable while another falls apart after a few interactions?

The more I built, the more familiar it all felt.

Context started to look like another engineering problem. Agent loops reminded me of distributed workflows. Evaluations felt like testing. Tool calling looked a lot like integrating external services, except the caller was now probabilistic.

AI engineering wasn’t replacing the engineering I already knew. It was building on top of it.

So I started experimenting.

I built RAG pipelines that failed in unexpected ways. I spent hours debugging retrieval when the real issue was somewhere else. I tried different agent architectures, prompt strategies, and context management techniques. Some ideas worked surprisingly well. Others failed spectacularly.

Each experiment taught me something I couldn’t have learned from documentation alone.

That’s why I’m starting How I AI.

This isn’t a tutorial series, and it isn’t a collection of prompt hacks.

It’s a journal of real engineering decisions, experiments, mistakes, and lessons learned while building AI systems from scratch.

If you’re a software engineer who’s curious about AI beyond the hype, I hope you’ll find something useful here.

Let’s build. 🚀