# Runs and scoring

> What happens during a benchmark run — real AI responses, claim-level scoring, and evidence you can audit.

- Section: Core concepts
- Updated: 2026-07-10
- Canonical: https://knitknot.ai/docs/runs-and-scoring/
- Publisher: KnitKnot, the AI Presence Management platform (https://knitknot.ai)

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A run is one full benchmark: your active prompt library executed across ChatGPT, Claude, Perplexity, and Gemini, every response scored, and the results rolled into your report. Runs typically take a few hours end to end, and you can watch progress live in the console.

## Real responses, not simulations

Each prompt is executed against its selected supported engines. KnitKnot stores the returned answer, citations, engine, and model version as a controlled benchmark record. Consumer chat sessions can differ because of personalization, geography, model version, and execution path.

## Claim-level scoring

Responses aren't graded with a single thumbs-up. A scoring pipeline breaks each response down:

- **Presence** — how central were you to the answer, from primary subject to absent.
- **Claims** — every substantive statement about you is extracted with its verbatim quote, marked accurate, inaccurate, or outdated, and where the response cites sources, tied to the URL that fed it.
- **Head-to-head verdict** — for comparison prompts, who the AI actually picked, per feature and overall. Verdicts are conservative: hedged answers count as ties, not wins.
- **Sentiment and framing** — how favorably you're characterized when you appear.

Scoring uses an AI judge for the semantic work — deciding whether a paragraph is a recommendation is not a keyword-matching problem — and the judge itself is continuously calibrated against a hand-labeled gold set.

## Written once, read everywhere

Scoring results are written down once per response, and every surface — report, dashboard, drill-downs, trend charts — reads the same records. The headline number and the list of evaluations behind it can't disagree, because they're the same data.

## Auditable by design

Every aggregate drills to its evidence: score → evaluations → response text → claim → cited source. When your report says you lost a comparison, you can read the exact sentences where the AI picked the other vendor. If a scored result looks wrong, you can re-score an individual response from the console.

## Cadence

Benchmarks re-run on a schedule so your score trends without manual effort, and you can trigger a run any time — most usefully after shipping a fix, to check whether the AI's answers moved. See [Issues and playbooks](/docs/issues-and-playbooks/) for how that loop closes.