How to Build a Reliable Eval Suite for LLM-Powered Product Features

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Adding evaluations to large language model features is essential for shipping reliable products, not just demos, since LLMs are non-deterministic and can silently degrade with every prompt change or model update. The process begins by defining a clear, business-oriented success criterion — such as whether an AI calling agent successfully booked a meeting — rather than vague technical measures. Developers then build a curated dataset of real test cases, including edge cases like interruptions or ambiguous responses, to serve as a concrete specification for expected behavior. Frameworks like DeepEval or Evidently AI can be used to structure metrics and automate evaluation checks within continuous integration pipelines. The approach transforms unpredictable LLM output into measurable, trackable signals that help teams catch regressions before they reach users.

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