#NDCG

Articles about NDCG — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with ndcg. ← All posts

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Pratik Dhanave · ·6 min read

Evaluating Search — How Do You Know Your Results Are Good?

You changed the ranking function and the results look different — but are they better? Search quality feels subjective, yet the entire field rests on measuring it rigorously, because you cannot improve what you cannot measure. Evaluating search means quantifying relevance with metrics that respect what users actually care about: not just whether the right documents appear, but whether they appear near the top. This post is about measuring relevance honestly, offline and online.

You changed the ranking and results look different — but are they better? Precision and recall ignore order (fatal for a ranked list), so rank-aware metrics (precision@k, MRR, NDCG) rule. Offline test collections for fast iteration, online A/B tests as ground truth — the same metrics that evaluate RAG retrieval.

#Information Retrieval#Evaluation#NDCG#Search Quality#Search#RAG#Testing

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.