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Regression detection for agents is fundamentally different from traditional software testing, because LLM outputs are stochastic and typically rely on semantic similarity comparison, LLM-as-judge, or predefined scoring rubrics.
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9/29/2026
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UnverifiedThere are currently two main approaches in the industry for testing LLM applications: soft assertions based on semantic similarity, and using another LLM as an evaluator to score outputs.78% similarUnverifiedFor highly subjective differences (such as generated text quality), blind comparative evaluation can be conducted using manual assessment or the LLM-as-judge approach.72% similarUnverifiedThe developer added a similarity threshold check before LLM processing, so that only retrieval results with similarity exceeding the set threshold are passed to the LLM.71% similarUnverifiedTesting large models/agents differs fundamentally from testing deterministic systems: traditional testing relies on deterministic assertions of "given input → expected output," whereas large model outputs have randomness (controlled by temperature) and context dependency.70% similar
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