In this article, we’ll explore why LLM evaluation is challenging, the different types of evaluations available, key concepts to understand, and practical guidance on setting up an evaluation process.

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LLM evaluation (evals) is essential for measuring model performance but challenging due to probabilistic outputs and subjective language quality. The guide covers three evaluation types: automatic (exact matching, semantic similarity, model-based judging), human (preference ranking, Likert scales), and benchmark-based (MMLU, HumanEval). Key concepts include choosing appropriate metrics, building quality test datasets, and accounting for statistical variance. A practical eval process involves defining success criteria, creating 50-100 diverse test cases, choosing evaluation approaches, iterating on improvements, and tracking performance over time. Common pitfalls include overfitting to eval sets, gaming metrics, and neglecting edge cases.

A Guide to LLM Evals

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