AI Engineering & LLMOps: From Prototype to Production: The Complete Practitioner's Guide (The AI Practitioner Library) by James P. Garland
English | March 4, 2026 | ISBN: N/A | ASIN: B0GR6H4RQ1 | 552 pages | EPUB | 1.31 Mb
AI Engineering & LLMOps: From Prototype to Production. The Complete Practitioner's Guide
Getting a language model to produce impressive output in a notebook is easy. Getting that same system to serve thousands of real users reliably, at acceptable cost, with observable behavior and a clear path to continuous improvement, that is engineering. This book closes the gap.
Written for ML engineers and data scientists who have already built LLM prototypes and now face the harder problem of production, AI Engineering & LLMOps is the most comprehensive practical guide to the full lifecycle of LLM-powered systems: build, deploy, monitor, and improve. Every chapter opens with a real production failure scenario, the kind that only surfaces after launch, and builds from that failure to the engineering practice that prevents it.
What this book covers
roduction RAG: Why naive retrieval fails at scale, and the advanced chunking, re-ranking, and context assembly strategies that make RAG systems actually workFine-tuning for production: The decision framework for when fine-tuning beats prompt engineering, plus the data engineering, training infrastructure, and model versioning practices that make fine-tuned models operationally sustainablePrompt engineering at scale: How to version, test, A/B test, and deploy prompts the way you deploy code, with regression suites, canary releases, and rollback plansAgentic systems: The compounding failure modes of tool-using agents in production and the safety, observability, and control patterns that contain themEvaluation frameworks: The full evaluation stack, reference-based metrics, LLM-as-judge, RAG decomposition, and continuous evaluation pipelines that run on production trafficSafety and guardrails: Input and output guardrail architectures, injection defense, PII handling, and red team practices calibrated to deployment riskThe improvement loop: Feedback pipelines, data flywheels, automated prompt optimization, and model version management, the compounding machinery that separates teams that plateau from teams that improveGovernance and compliance: GDPR and HIPAA compliance patterns, right-to-erasure implementation, change management, and the organizational maturity model that maps where your team is and where it needs to goThe PRISM framework, Performance, Reliability, Intelligence, Safety, Maintainability, runs through every chapter as a consistent lens for evaluating engineering decisions and understanding trade-offs. Every tool recommendation includes its primary production failure mode. Every technique includes the conditions under which it breaks down.What this book is not: An introduction to what language models are. A survey of AI capabilities. A beginner's guide to prompting. This book starts where the other books stop, at the point where your prototype works and the hard engineering begins.
If you have built an LLM prototype and need to take it to production, this is the book you reach for first.
From the book: "A model that impresses in a notebook will humiliate you in production. The gap between demo quality and production quality is not a gap in model capability, it is a gap in engineering."
Also includes:40-question Production Readiness Checklist across all PRISM dimensions7 canonical
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