Search Quality: Precision@k, Recall@k & Hit Rate
This lesson is part of the course
In this lesson
You score six help-centre queries with precision@k and recall@k, with k kept in one cell so you can change the cut-off, average them over the queries and calculate the hit rate a RAG pipeline cares about.
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Lessons in this course
- Search Quality: Precision@k, Recall@k & Hit Rate Locked
- Mean Reciprocal Rank: How Soon Is the First Good Answer? Locked
- NDCG@k: Graded Relevance and the Ideal Ranking Locked
- Average Precision & MAP: Rewarding Good Order Locked
- Thresholds, the ROC Curve & AUC by Trapezoids Locked
- Calibration: Does 90% Confident Mean 90% Correct? Locked
- LLM-as-a-Judge: Percent Agreement & Cohen's Kappa Locked
- A/B Testing Two Models: Win Rate, Ties & a Confidence Interval Locked