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When you evaluate a search or RAG system, two independent questions apply — and collapsing them into one number hides where the problem is:
  • Retrieval quality — did the system find the right documents? Ranking, coverage, and source quality. A wrong answer built from missing or irrelevant sources is a retrieval limitation.
  • Synthesis quality — is the produced answer good? Faithfulness, completeness, correctness, and presentation. A wrong answer built from correct sources is a synthesis problem — fixable by changing the generation model.
Every built-in scorer is tagged with the dimension it measures, so RunAgain can report the two separately.

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Which scorers land where

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Weighted composite

Different teams weight quality differently — a healthcare app leans on faithfulness, a news app on freshness. On Experiments → Compare, the Weighted composite panel lets you assign a weight to each scorer and see the overall and per-dimension composite recompute instantly — no re-running the eval, because it works from the cached per-scorer means. Set a weight to 0 to drop a scorer entirely. The composite is a weighted mean of the scorer values; the retrieval and synthesis composites are computed over their own scorers so you always keep the two views side by side.