Overview

LLM Engineering in Production is written for professional software and ML engineers, it goes beyond demos and quickstarts to address the real engineering problems: reliability, cost, evaluation, safety, and the operational discipline required to ship LLM systems that hold up under real-world pressure. Inside this book, you'll learn how to: Master context engineering - the discipline that has replaced prompt engineering as the core skill in production LLM work Design and operate RAG pipelines with hybrid search, re-ranking, and provenance tracking Build rigorous evaluation frameworks from first principles, including LLM-as-judge techniques Measure and mitigate hallucination using grounding strategies and structured attribution patterns Route intelligently across model fleets to balance cost, latency, and capability Red-team your systems against prompt injection, jailbreaks, and data exfiltration risks Along the way, you'll build: A structured model selection framework covering frontier APIs and open-weight models Regression testing pipelines that integrate with CI/CD and survive provider version changes Compliance-ready audit trails and data privacy patterns for regulated environments Multi-agent architectures designed for reliability, not just demos This book is for engineers who are comfortable with production systems and want to apply that discipline to LLM development. It doesn't make the field sound easier than it is - it explains why the hard problems are hard, what current best practice looks like, and what your realistic options are. Anchored to the 2026 production landscape, it focuses on principles and patterns that outlast any individual model release.

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