Relevance Judge

To score relevance, provide a monitoring topic and a batch of candidate entries to receive per-item relevance scores in the 0-100 range. This process outputs brief, per-item reasons to justify the assigned score for each candidate.

Automating alert triage requires deterministic relevance scoring to evaluate how closely candidate items match your topic. This approach assigns a 0-100 score with concise justification per item, guiding downstream processing decisions efficiently.

Yes, deterministic relevance scoring is ideal for knowledge-graph curation workflows. By evaluating candidate entries against a given topic, it provides per-item 0-100 scores and concise reasons to guide what gets included or filtered.

Radar-style monitoring uses per-item relevance judgments to quantify how closely each candidate item matches a monitoring topic. It generates deterministic 0-100 relevance scores with concise justification to enable fast triage and downstream decision-making.

You need to quantify candidate relevance when managing large volumes of alerts or entries that require fast triage. Assigning deterministic 0-100 relevance scores with concise reasons enables efficient downstream decision-making and knowledge curation.