LLM Application Bible 5 in 1: Foundations, RAG, Fine-Tuning, Evaluation, and Production Deployment for AI Engineers

This book is not a collection of code snippets that you can copy and paste. The real value lies in the decision frameworks, tradeoff analyses, and hard-won practical advice that comes from building systems that serve real users. You will not find lists of tools or bullet-point summaries because those age poorly and encourage shallow thinking. You will find explanations of why certain approaches work, when they break, and how to make judgment calls when there is no clear answer.

You will learn how to choose the right model for your use case, build retrieval systems that actually work, and decide when to fine-tune versus when to use prompt engineering. You will master evaluation pipelines that catch regressions before they reach users, infrastructure decisions that determine cost and latency, and monitoring that keeps your system healthy in production. This book draws on years of experience building production LLM applications and includes the mistakes that taught the most important lessons.

Inside, you'll discover:

• The LLM application stack and how each layer interacts

• How to choose the right foundation model for your specific use case

• Prompt engineering as an engineering discipline with testing and versioning

• Building retrieval-augmented generation systems that work in practice

• Advanced RAG patterns including iterative and agentic retrieval

• When to fine-tune and how to do it efficiently with LoRA and QLoRA

• Evaluation frameworks, hallucination detection, and production deployment

The field moves fast, but the principles in this book are designed to last. The specific model names and tool versions will change, but the underlying patterns of retrieval, fine-tuning, evaluation, and deployment will remain relevant. My goal is to make you a better engineer, not someone who can follow a tutorial.