recall
AI, ML & GenAI 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 F1Precision is what fraction of your positive predictions were correct; recall is what fraction of the actual positives you caught. They trade off as you slide the decision threshold, and which one matters depends on the cost of false positives vs false negatives. F1 is their harmonic mean. On imbalanced data, accuracy misleads and these metrics (with PR-AUC) tell the truth. AI, ML, and GenAI engineer interviews probe them because choosing and tuning the threshold by business cost is a core, constantly-tested skill.📊 Evaluation & ML Foundations
Foundational
RAG EvaluationEvaluating a RAG system means scoring 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 gets scored with recall@k (the ceiling for the whole system), precision, and rank metrics; generation gets scored for faithfulness (is each claim supported by the context?) and answer quality. AI, ML, and GenAI engineer interviews probe it because measuring RAG end-to-end, and knowing which half failed, is the core debugging skill.📊 Evaluation & ML Foundations