Dataset Search Quality Evals

Problem

Search can feel better while quietly missing the right dataset, burying a useful match, or regressing on an important query. A demo is not a baseline.

My role

Worked from the user’s discovery task to define what a good result meant, how to label cases, and how to inspect matches, misses, rankings, and regressions.

What was built

A repeatable evaluation baseline for search quality, designed to support product decisions about relevance, ranking, and future retrieval changes.

Architecture

representative queries → expected matches → ranked results → error review → regression set

The evaluation is the product instrument, not a decorative score.

Evaluation & controls

Keep cases labelled, separate direct matches from paraphrases, and review misses instead of reducing the story to one aggregate number.

Trade-offs

A smaller, well-understood set of cases is more useful early than a large opaque benchmark. It limits coverage, but makes failures actionable.

Evidence of completion

This remains a portfolio case outline rooted in prior search-quality work, not a claim that a standalone public evaluation service is shipped.

Next step

Publish a redacted example with synthetic queries, expected evidence, ranking views, and a regression check that a reviewer can reproduce.