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Evaluating LLM Outputs Without Guessing

a lightweight method for testing AI features with a small set of real examples and clear pass criteria.

Muhammad Yasir

@YasirAwan4831

Published 1 min read

Evaluating LLM Outputs Without Guessing

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Shipping an AI feature without tests means every prompt change is a gamble. A small evaluation set turns opinions into evidence.

Build a small evaluation set

  • Collect twenty to fifty real inputs, including awkward ones.

  • Write down what a good answer must contain.

  • Re-run the set after every prompt or model change.

Score what matters

Decide in advance which behaviors count as a pass, such as citing a source, refusing to invent facts or returning valid JSON, and track the pass rate over time.

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