check-test-logical-resp-claude

To find logical inconsistencies in passed test results, you audit test-results JSON files to surface semantic mismatches where responses are off-topic or contradict the prompt. This evaluates topic accuracy, entity usage, numeric consistency, and intent alignment without re-checking factual assertions.

Semantic mismatch detection in multi-model test results identifies passed responses that are logically incoherent or misaligned with the original prompt. It analyzes conversation history, response text, and function call details to assess intent alignment and entity accuracy across different models.

You audit test-results JSON files by parsing testCase.prompt, testCase.conversationHistory, and response fields to evaluate intent alignment. The audit resolves files like test-results_*.json and multi-model-results_*.json to find semantic gaps in logically incoherent responses.

Yes, you can audit test result JSON files to validate numeric consistency and entity usage by analyzing optional functionCallDetails alongside conversation history. This surfaces illogical responses in passed tests by checking topic accuracy and correct entity usage across multi-model results.

The limitation of auditing passed test results for logical coherence is that it does not re-check factual assertions. It strictly surfaces semantic mismatches like off-topic responses or contradictions, meaning factual accuracy validation must be handled by a separate testing mechanism.