(Estimated Content Breakdown: 70% Human, 30% AI-Assisted)
[Affiliate Disclosure: This post contains an affiliate link to the book on Amazon. If you purchase through this link, I may earn a small commission at no extra cost to you.]
I recently finished reading and implementing the core concepts from Build a Large Language Model (From Scratch) — a hands-on guide to understanding and building LLMs from the ground up. If you consider yourself a first principles thinker, or if you’re trying to wrap your head around how these modern AI systems actually work, I highly recommend giving this book a look.
We’re in an age where every product pitch includes “AI-powered” as a selling point, and everyone seems to be either terrified of or wildly overhyping large language models. This book, refreshingly, cuts through the hype. It’s technical, but not overwhelming. It walks you through real implementations and introduces critical concepts like tokenization, attention mechanisms, and transformer architecture in a way that doesn’t assume you’ve already worked at OpenAI.
Why This Book Helped Me Level Up
Before reading this, I had a high-level conceptual understanding of LLMs — I could explain what they did and why they were useful, but I didn’t fully get what was going on under the hood. This book changed that.
The explanations of attention mechanisms, in particular, helped me take a big step forward. While I still don’t think I’ve internalized every aspect of how attention works — in my head, it’s still something like “cool matrix math that lets related concepts land near each other in a learned output space” — I’ve realized that’s actually enough to start using the tools meaningfully. Not everything needs to be understood at the graduate math level to be useful in practice.
It also demystified the process of training and fine-tuning. The toy example presented in the book helped me go from zero to running a simplified model on my own hardware — a big milestone. It’s not meant to compete with GPT-4, obviously, but it’s real enough to spark meaningful experiments.
Where I Wanted More
If there’s one thing I wish the book had expanded on, it’s how to use these techniques with existing models. There’s a lot of focus on training your own model, but less on extending or fine-tuning third-party models using frameworks like PyTorch. That’s a missed opportunity, especially for practitioners like me who aren’t trying to compete with OpenAI, but do want to integrate LLMs meaningfully into apps and workflows.
That said, the book does a good job of setting the foundation. I now feel better equipped to dive into those more advanced topics because I understand the architecture and training process at a much deeper level.
Final Thoughts
This book won’t turn you into an AI research scientist overnight, but that’s not the point. What it will do is make LLMs feel less like magic and more like something you can work with, build on, and reason about. For engineers, data scientists, and anyone interested in understanding AI from the inside out, that’s incredibly empowering.
I’d estimate this blog post is about 70% “human-written” content — my thoughts, my experience — and about 30% AI assistance, mostly in refining phrasing and structure. Fitting, perhaps, for a post about learning how to build the very tools that helped shape it.
If you’re even a little curious about how LLMs actually function, and you like learning by doing, pick up Build a Large Language Model (From Scratch).
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