Large Language Models in Practice

Large Language Models in Practice is a hands-on engineering guide
for developers who want to go beyond ChatGPT prompts and build
real, production-ready AI systems from scratch, with working code.

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
with Python code you can run, modify, and ship.

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,
and enterprise-grade architecture

10 hands-on projects including a PDF Q&A system, AI customer
support chatbot, personal research assistant, and a deployable
enterprise AI assistant.

This is not a theory textbook. This is the book you hand to
a developer and say: build something real with it.

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.