LLM Evaluation Engineering: Reliable Tests, Regression Gates, and Quality Metrics for Production AI
English | October 5, 2026 | ASIN: B0HLZD9JZF | 117 Pages | Epub | 304.34 KB
The demo looks good. Should you ship?A fluent answer can hide the wrong policy, an unsupported claim, or an unauthorized action. A higher average score can conceal a regression in the cases that matter most.LLM Evaluation Engineering shows AI engineers, QA and SDET practitioners, and technical product leaders how to replace intuition-driven releases with a repeatable quality process.You will learn how to:- Turn product requirements into observable behaviors and explicit acceptance criteria.- Build versioned datasets with useful coverage, protected holdouts, and clear limits on what the results prove.- Write deterministic graders, calibrate human rubrics, and audit model judges for consequential errors.- Compare candidates with paired evidence, confidence intervals, and predeclared regression margins.- Evaluate retrieval, claim support, agent state, authorization, retries, and execution budgets.- Build release gates and production feedback loops that keep critical failures visible.Apply the method as you read. Fourteen focused chapters include explained decision exercises. A fifty-failure-mode workbook and twelve operating templates help you design your own evaluation system. The complete standard-library Python lab is reproduced in the book: its candidate improves the overall score, yet the gate correctly blocks a critical regression.The approach is vendor-neutral. All cases and release results are original fictional fixtures, and the lab requires no API key or paid evaluation platform. Basic Python, JSON, and software-testing knowledge are helpful; no statistics degree is required.Build evidence you can inspect. Know what your scores prove. Make the next AI release a decision you can defend.
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