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Evaluating language model applications and acting on the results: building a test set from real questions with agreed reference answers, scoring retrieval and answers separately, reading results by slice, and then turning evaluation into release gates, monitoring and rollback for production.
Showing · #LLM Evaluation
To evaluate an LLM application for hallucinations, test it on real questions with agreed reference answers, check every claim against its sources, score retrieval and answers separately and read the results by slice, not as one average.
Testing cannot remove hallucinations entirely, so production safety depends on what surrounds each release: a gate agreed in advance, staged rollout, monitoring of live answers, incident containment and a rehearsed rollback.
