Hands-On Large Language Models: Building and Using LLMs from Scratch with Python

Name: Hands-On Large Language Models: Building and Using LLMs from Scratch with Python
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Bol What if you could stop reading about AI and start building it?Large language models have quietly become the most important technology of our generation - powering everything from customer service to code generation to scientific discovery. But most books on the subject leave you stuck in one of two places: drowning in academic theory with no code in sight, or copy-pasting API calls with no real understanding of what's happening underneath.Hands-On Large Language Models is different. It's the guide the author wishes existed when they started: a complete, practical path from "What actually is a transformer?" to building, fine-tuning, evaluating, and deploying real LLM-powered applications - with working Python code in every single chapter.Whether you're a developer pivoting into AI, a student trying to get ahead of the curve, or an engineer who wants to move beyond prompt-and-pray development, this book gives you both the why and the how.Inside, you'll learn how to: - Understand exactly how transformers, tokens, embeddings, and attention actually work - no hand-waving, no PhD required- Set up a professional LLM development environment with proper credential security and cost controls- Master prompt engineering as a real, testable engineering discipline - not guesswork- Build production-grade applications with conversation management, streaming, and error handling- Build a Retrieval-Augmented Generation (RAG) pipeline from scratch, including chunking, embeddings, and vector databases- Fine-tune open-source models using LoRA and QLoRA - even on modest hardware- Evaluate LLM outputs systematically, catch hallucinations, and build regression tests that actually work- Build autonomous AI agents that reason, use tools, and complete multi-step tasks safely- Deploy, monitor, and scale your LLM applications in production - including defending against prompt injectionEvery chapter combines deep conceptual grounding with full, runnable code examples - so you're never just reading about a concept, you're building it, breaking it, and understanding exactly why it works.By the end of this book, large language models will stop feeling like a mysterious black box and start feeling like exactly what they are: a powerful, understandable tool you can bend to your own ideas.This book is for you if: You know some Python and want a serious, structured path into LLM engineering You're tired of tutorials that skip the "why" and just paste API keys You want to build real, deployable applications - not just toy demos You want to understand LLMs deeply enough to debug them, not just use themStop reading about AI from the sidelines. Open your editor, and start building.

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What if you could stop reading about AI and start building it?Large language models have quietly become the most important technology of our generation - powering everything from customer service to code generation to scientific discovery. But most books on the subject leave you stuck in one of two places: drowning in academic theory with no code in sight, or copy-pasting API calls with no real understanding of what's happening underneath.Hands-On Large Language Models is different. It's the guide the author wishes existed when they started: a complete, practical path from "What actually is a transformer?" to building, fine-tuning, evaluating, and deploying real LLM-powered applications - with working Python code in every single chapter.Whether you're a developer pivoting into AI, a student trying to get ahead of the curve, or an engineer who wants to move beyond prompt-and-pray development, this book gives you both the why and the how.Inside, you'll learn how to: - Understand exactly how transformers, tokens, embeddings, and attention actually work - no hand-waving, no PhD required- Set up a professional LLM development environment with proper credential security and cost controls- Master prompt engineering as a real, testable engineering discipline - not guesswork- Build production-grade applications with conversation management, streaming, and error handling- Build a Retrieval-Augmented Generation (RAG) pipeline from scratch, including chunking, embeddings, and vector databases- Fine-tune open-source models using LoRA and QLoRA - even on modest hardware- Evaluate LLM outputs systematically, catch hallucinations, and build regression tests that actually work- Build autonomous AI agents that reason, use tools, and complete multi-step tasks safely- Deploy, monitor, and scale your LLM applications in production - including defending against prompt injectionEvery chapter combines deep conceptual grounding with full, runnable code examples - so you're never just reading about a concept, you're building it, breaking it, and understanding exactly why it works.By the end of this book, large language models will stop feeling like a mysterious black box and start feeling like exactly what they are: a powerful, understandable tool you can bend to your own ideas.This book is for you if: You know some Python and want a serious, structured path into LLM engineering You're tired of tutorials that skip the "why" and just paste API keys You want to build real, deployable applications - not just toy demos You want to understand LLMs deeply enough to debug them, not just use themStop reading about AI from the sidelines. Open your editor, and start building.

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