AI Engineering

I strongly believe that if you want to use any technology effectively, you need to understand how it works under the hood. Especially in software engineering.

So if you haven’t looked into LLM internals yet, I’d highly recommend reading AI Engineering by Chip Huyen. The book was published in December 2024. And as AI is moving extremely fast, you might think it’s already outdated. Yes and no.

The book focuses on fundamentals. And they don’t really change that fast. You won’t find hype topics like skills, harnesses, or agents orchestration there. But for building structured understanding of how AI works, you don't actually need them.

What I personally found useful:
🔸 Core LLM concepts: tokenization, training and post-training processes, datasets preparation. This part is very similar to Mashing Learning Crash Course from Google.
🔸 Model evaluation: quite complex but interesting topic about model output results and their comparison. The book covers ranking, model specialization, public benchmarks and AI-as-a-judge approach.
🔸 Prompt engineering: good reference about context and prompting. Additionally, the author described different security aspects of using prompts, that part really extended my thoughts about what can go wrong.
🔸 Finetuning: a deep dive into different ways to optimize models. You need to be a good mathematician to understand this part. So I was really glad I'm not an ML engineer 😃 (huge respect to all ML experts, it's really hard).
🔸 User feedback: basic patterns on how to collect feedback, what to measure and why, common pitfalls.

To sum up, this book is really great to structure your knowledge about modern AI systems. Once you have that foundation, it becomes much easier to navigate all the new tools, patterns and paradigms that appear almost every month.

#booknook #ai #engineering