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DESCRIPTION:Your prompts are part of your codebase now. Start treating them like it.
Every developer building with LLMs eventually hits the same wall:
a prompt that worked perfectly in testing falls apart the moment real users, real edge cases, and real bills show up.
This book closes that gap. It is not a collection of clever tricks or vague "think like the model" advice, it is a practical engineering manual for building LLM-powered features that actually survive production.
Across 14 hands-on chapters, you will build a complete toolkit using real, runnable code:
structuring prompts for consistent output, forcing structured JSON you can actually parse, grounding models in your own data with retrieval-augmented generation, giving models real tool access, managing conversation memory without blowing your token budget, testing prompts like the code they are, and defending your application against prompt injection and bad input.
The final capstone chapter combines everything into a working AI code review assistant that reads a git diff, retrieves relevant codebase context, and returns a structured, validated review.
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