Improve Retrieval when Few Chunks are Returned | Dataford Interview Questions

Your question is Improve Retrieval when Few Chunks are Returned. Take a moment with it on the right.

Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).

Problem

Suppose there are 10 potentially relevant chunks, but your retriever returns only 2 relevant chunks. How would you improve the retrieval pipeline

Asked in the Round 2 stage. Explain how you would diagnose the recall problem, change the retrieval stages, and validate that the solution improves relevant context coverage without unacceptable latency or noise.

Deliverables

Clarify the retrieval objective, relevance definition, and latency constraints.
Identify likely causes across chunking, indexing, query processing, and retrieval models.
Propose an improved retrieval pipeline, including candidate generation, ranking, and optional re-ranking.
Define offline and online evaluation, feedback collection, and monitoring.
Discuss failure modes such as feature drift, stale indexes, and training-serving skew.

Your question is Improve Retrieval when Few Chunks are Returned. Take a moment with it on the right.

Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).

Suppose there are 10 potentially relevant chunks, but your retriever returns only 2 relevant chunks. How would you improve the retrieval pipeline

Deliverables

Clarify the retrieval objective, relevance definition, and latency constraints.
Identify likely causes across chunking, indexing, query processing, and retrieval models.
Propose an improved retrieval pipeline, including candidate generation, ranking, and optional re-ranking.
Define offline and online evaluation, feedback collection, and monitoring.
Discuss failure modes such as feature drift, stale indexes, and training-serving skew.