Applied AI Engineering: Retrieval, Fine-Tuning, Evaluation, and Deployment Patterns for Real-World LLM Systems

Stop building demos. Start building systems that survive real users.

The gap between a working prototype and a production AI system is the hardest part of engineering with large language models, and it is the part most resources gloss over. This book fills that gap with battle-tested patterns for retrieval, fine-tuning, evaluation, and deployment that have survived contact with real users.

Inside, you will learn:

When to use retrieval versus fine-tuning, and how to do both

How to build production RAG systems that actually work

Advanced retrieval patterns including hybrid search, reranking, and iterative retrieval

Fine-tuning strategy and data preparation for real results

Parameter-efficient fine-tuning with LoRA and QLoRA

Evaluation as an engineering practice, including LLM-as-judge and critique shadowing

Deployment patterns including caching, batching, and model routing

Cost optimization and intelligent model routing strategies

Guardrails and safety architecture for production systems

Monitoring and observability for LLM systems

This is not a theoretical book. Every recommendation comes from direct experience building production LLM systems across multiple organizations. When the book tells you something works, it is because that approach has survived contact with real users. When it tells you something does not work, it is because the author has seen the failure firsthand.

Whether you are a software engineer building AI features for the first time, an engineering leader making strategic decisions, or an experienced ML practitioner adapting to LLMs, this book gives you the decision frameworks you need.

The landscape of AI tools changes constantly, but the patterns in this book endure. Start with retrieval. Measure your errors. Use fine-tuning to close the gaps. Deploy with confidence. That is the pattern that never goes out of style.