Build a tiny embedding eval
Create a lightweight retrieval eval using representative queries, labeled relevant sources, and top-k metrics.
The team needs to know whether semantic search is good enough for internal policy questions, but only has hand-picked demo queries. Representative queries + labeled relevant sources + top-k metrics create a practical embedding eval. The common trap is evaluating only on queries that already look good in the demo. That hides exact-term misses, stale-source problems, and scope errors. Sample Collect 30 queries from real support logs and stakeholder scenarios. The sample should include easy paraphrases, exact IDs or artifact names, and scope-sensitive policy questions. Label For each query, mark one or more relevant documents or chunks before running the…
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