35Explain the RAGAS evaluation dimensions, faithfulness, answer relevance, context precision, context recall, and when to trust them▼mediumOpenAIScale1 replies◆ premiumReciting the four definitions is table stakes; the differentiator is knowing which two need ground truth, what each dimension tells you to fix, and where the judge-model scores quietly lie. The 2x2 that turns RAGAS into a debugging tool.Open full answer →
33How do you measure whether a RAG pipeline is actually working? Walk me through the metrics.▼hardDatabricksGoldman SachsOpenAI2 replies◆ premiumThe 2026 successor to 'explain precision and recall', interviewers now test whether your classical ML evaluation discipline survives contact with GenAI. The decomposition, the RAGAS dimensions, and the judge-calibration step everyone skips.Open full answer →
