Evals for LLM Apps A Practical Guide to Testing AI Features Before Your Users Do [#1042173]

Evals for LLM Apps: A Practical Guide to Testing AI Features Before Your Users Do
English | October 14, 2026 | ASIN: B0HN1JP9ZS | 335 pages | Epub | 2.56 MB
Your AI feature passed every test. Then your users found the bug.You changed one line in a prompt, or your model provider shipped a new version, and the assistant started giving customers the wrong answer. Your unit tests stayed green, because they never checked what the model actually said. Nobody noticed for ten days.Evals for LLM Apps shows software developers how to build the missing safety net: an eval suite that measures how often your AI feature gets it right, catches regressions before they ship, and keeps watching once real users arrive.Written by an engineering manager and test-driven development practitioner, this book treats evals the way good engineers treat tests. Start from real failures. Automate the checks. Run them on every change. Trust the numbers only after you have checked them.Inside, you'll learn how to:Read your traces first and turn real failures into a taxonomy worth measuringBuild golden datasets from production conversations and targeted synthetic dataWrite code-based graders for everything a rule can checkRun human review with clear guidelines and measured agreement between reviewersUse an LLM as a judge, then calibrate it against human labels so you know how far to trust itEvaluate RAG systems: retrieval quality, grounded answers and accurate citationsEvaluate agents on their trajectories, tool calls and final outcomes, not just the last messageGate pull requests in CI with thresholds that survive non-deterministic outputsRun online evals and A/B tests, monitor quality in production, and feed failures back into your datasetsBuild red-teaming suites that show how your app behaves under attackChoose your tools with an honest comparison of open-source frameworks, hosted platforms and a homegrown harnessOne running example ties it all together: a fictional marketplace's support assistant that answers from help-center articles and can look up orders and issue refunds. Every chapter adds a piece to its eval suite, with short, runnable code you can adapt on Monday.No hype. No vendor lock-in. No math you can't follow: every metric comes with its formula and a worked example.If you ship LLM features and can't say whether your last change made them better or worse, this book is for you.Get your copy and make your next prompt change with confidence.