LLM testing guide focuses on expected behaviors, not exact outputs

This guide introduces a method for testing Large Language Models (LLMs) by defining expected behaviors rather than exact outputs. It proposes a three-part template: 'Must' (essential actions), 'Must not' (specific failures to avoid), and 'Okay' (acceptable variations). This approach addresses the variability of LLM responses, ensuring tests focus on functional correctness rather than precise wording. The article provides examples and rules for creating effective, checkable expected behaviors, drawing parallels to traditional software testing. AI

IMPACT Provides a framework for improving the reliability and testability of LLM applications.

RANK_REASON Guide on testing LLMs, not a new model release or core research.

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