Build a Large Language Model (from Scratch)

Sebastian Raschka · 2026-07-14

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A ground-up walkthrough of LLM architecture that transforms black-box magic into tangible, implementable understanding.

What it covers

Raschka guides you through building an LLM from first principles—tokenization, attention mechanisms, transformer architecture, and training pipelines—with working code examples throughout. It's not theoretical hand-waving; you're literally constructing the pieces that power modern language models.

Why it matters

In 2026, the gap between 'using APIs' and 'understanding what's actually happening' has become a liability. As production AI systems become more complex and safety-critical, practitioners who can reason about model internals—not just prompt engineering—are the ones shipping reliable systems. This book closes that gap.

What it made me think

Reading this felt like finally getting the owner's manual after years of driving the car. It reminded me that true agency in AI—whether you're debugging a model or designing an agentic system—comes from understanding the substrate. You can't build trustworthy multi-agent systems if you don't know how the individual agents actually think.

The short version

You can't build reliable agentic systems if you don't know how the brain works.

My take

Debugging agentic systems makes clear that surface-level knowledge breaks down fast. The moment you need to optimize inference, reduce hallucination, or understand why two models behave differently on the same prompt, you're back to fundamentals. This book is that foundation. It's the difference between being a user of LLMs and being someone who can reason about them—and in a field moving this fast, reasoning beats rote knowledge every time.

How it connects

• Understanding attention mechanisms directly informs how you structure multi-agent communication and reasoning chains
• Transformer internals reveal why certain evaluation metrics work and others miss critical failure modes
• Training dynamics knowledge is essential for fine-tuning agents to behave reliably in production environments

Bottom line

If you're building agents or production systems, read this—not to become a researcher, but to stop being surprised by your own models.

Takeaways

• Attention is the core primitive—understanding it deeply unlocks intuition for why transformers work and where they fail
• Training dynamics matter as much as architecture; the journey from random weights to coherent outputs is where real learning happens
• Tokenization choices ripple through everything downstream; there's no 'neutral' encoding
• Building from scratch reveals the cost-benefit tradeoffs in real time—why certain architectural choices exist and what you sacrifice with alternatives
• Code-first learning sticks better than equations; you internalize constraints when you implement them yourself

Topics: llm, architecture, hands-on, reasoning, production-ai