LLM Evaluation and AI Reliability

Everybody traces. Almost nobody measures. The distance between those two facts is why your team still cannot answer whether your AI feature is any good.

You have a dashboard, a year of production traces, and a slide deck claiming performance is "generally positive with some issues." But when a stakeholder asks a direct question, you have no hard numbers-only an impression derived from reading transcripts.

Technical books about generative AI rot quickly. This is a vendor-neutral, battle-tested engineering manual written against the 2026 OpenTelemetry GenAI consensus. It cuts past marketing hype to give you a reproducible, rigorous evaluation framework.

As Volume Four of The Applied AI Series, LLM Evaluation and AI Reliability bridges the gap between raw observability and true quality measurement. It teaches you how to build an evaluation program that runs in CI, scores production traffic asynchronously, and produces numbers your team can actually trust.

Inside, you will discover how to:

• Deconstruct "Good": Break vague quality adjectives down into independent, binary, checkable properties across retrieval systems, multi-step agents, chat assistants, and data extraction pipelines.
• Build Honest Golden Sets: Gather 100 to 500 high-coverage examples weighted toward your edge cases, version them like code, and prevent silent data decay.
• Exhaust Deterministic Checks First: Catch structural schema drift, invalid tool arguments, and broken citations instantly using exact code before spending a single penny on model-based judges.
• Validate the Judge as an Instrument: Pin model versions, eliminate positional and verbosity biases, and calculate Cohen's kappa corrected for chance so you never ship a broken evaluation metric.
• Diagnose Retrieval vs. Generation Failures: Isolate context recall, context precision, corpus coverage gaps, and hallucination types (unsupported, contradicted, and fabricated claims).
• Evaluate Agents and Conversations: Track trajectory milestones, forbidden tool calls, multi-turn constraint retention, and simulated user personas that change their mind.
• Implement CI Gating & Online Scoring: Build a noisy-aware regression gate for pull requests, monitor live production streams per customer segment, and turn every production failure into a permanent test case.

Who This Book Is For:

Senior AI Engineers, ML Infrastructure Leads, Software Architects, and Backend Developers who manage production LLM features, semantic search, or enterprise AI agents and need deterministic metrics rather than trial-and-error prompt tuning.

Stop guessing if your prompts work. Write down what good means, validate your instrument, and build AI systems that stay reliable in production.