Large Language Models in Practice: How LLMs Work, to Use Them, and Build Production-Ready AI Systems

Most developers learn what LLMs can do. This book teaches you how >Large Language Models in Practice is a hands-on engineering guide for developers who want to go beyond ChatGPT prompts and build >You'll start with the fundamentals transformers, tokenization, embeddings, attention and progressively move into the engineering patterns that power real-world AI products. Every concept is paired >What's inside: - How LLMs actually work under the hood transformers, self-attention, positional encoding, and next-token prediction - Working with LLM APIs authentication, context windows, streaming, cost management, and rate limits - Prompt engineering that works zero-shot, few-shot, chain-of-thought, role-based prompting, and reusable templates - Building real AI apps chatbots, summarizers, content generators, and information extraction systems - Retrieval-Augmented Generation (RAG) vector databases, embeddings, document chunking, and full RAG pipelines - Fine-tuning open-source models for your specific use case - AI agents how to design, build, and orchestrate them - Production deployment scaling, monitoring, evaluation, >10 hands-on projects including a PDF Q&A system, AI customer support chatbot, personal research assistant, and a deployable >This is not a theory textbook. This is the book you hand to >Perfect for: software engineers, backend developers, technical founders, CS students, and self-taught developers who want to build serious AI systems no ML PhD required.