recall
Applied AI interview questions tagged recall, across every topic.
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Concepts behind "recall"
The curriculum that explains the ideas these questions test.
Foundational
Precision, Recall, and F1: Thresholds and Imbalanced DataPrecision is how many flagged items were right, recall is how many real positives you caught. How they trade off with the threshold, and why accuracy lies.📊 Evaluation & ML Foundations
Foundational
RAG EvaluationEvaluating a RAG system means evaluating retrieval and generation separately, because a bad answer is usually a retrieval failure (the right context was never fetched) and you cannot fix what you cannot localize. Retrieval is scored with recall@k (the ceiling for the whole system), precision, and rank metrics; generation is scored for faithfulness (is each claim supported by the context?) and answer quality. Applied-AI interviews probe it because measuring RAG end-to-end, and knowing which half failed, is the core debugging skill.📊 Evaluation & ML Foundations