How to Adapt an LLM Evaluation Framework for Any AI Pipeline
A developer has shared a reusable method for stress-testing large language models across different use cases, originally built for an order-reading AI. The framework centers on identifying the worst irreversible mistake a system could make — such as sending an incorrect auto-reply or overwriting data without a backup — and using that as the basis for grading AI errors. Failures are classified into four tiers: Fatal, Risky, Missed, and Harmless, based on whether a human can undo the outcome. Test questions are designed around traps like confusable data pairs, plausible non-targets, mid-message reversals, and memory-versus-new-information conflicts. The author recommends a minimum of one test case per identified accident type, noting that a starting set of around ten questions is sufficient before expanding further.
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