Practical Prompt Engineering for Developers

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.
Every code sample uses a real Anthropic-style SDK, is complete and runnable (not pseudocode), and reflects patterns used in actual production systems, not playground demos.
What you'll learn:
How to structure system and user prompts for consistent, reliable output
Forcing guaranteed structured output using tool schemas and Pydantic validation
Using few-shot examples to teach exact output formats
When chain-of-thought reasoning earns its cost, and when it's wasted tokens
Building retrieval-augmented prompts (RAG) grounded in your own documents or codebase
Giving models real tool-calling access to your own functions and APIs
Managing multi-turn conversation memory without runaway token costs
Estimating and controlling per-request cost, latency, and model selection
Writing golden-set tests and automated evals for prompts, just like unit tests
Defending against prompt injection with layered, architectural guardrails
Building multi-step agent workflows, and knowing when NOT to use one
Deploying LLM features safely with versioning, feature flags, and monitoring
Whether you're adding your first LLM API call to a side project or hardening a production AI feature against real-world abuse, this book gives you the exact patterns, code, and judgment calls to do it right. Part of the Practical Programming Series by Jacob Ren, written for developers who want to ship, not just experiment.

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