Worked Walkthrough: Build a Retrieval Eval Set
Design a compact retrieval evaluation set with queries, relevant records, and a ranked-list metric.
Build a first retrieval eval set for a sales-assistant vector database before tuning chunk size, top_k, and hybrid search. Representative queries + relevance judgments + ranked-list metric = retrieval decisions you can defend. The common trap is evaluating only demo questions. Demo questions are usually clean, broad, and written by people who know the corpus. Real users ask partial, specific, messy, and constraint-heavy questions. Collect real needs Pull 60 queries from support tickets, sales Slack, search logs, and SME interviews. The query source matters because retrieval should match how users ask, not how builders describe the corpus. Bucket query types…
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