Know what your docs are missing
BeforeQuery analytics show which questions succeed, which ones expose gaps, and how your AI answers score on automated groundedness evals — so your docs keep getting better with every user interaction.
Six dimensions of knowledge health
From raw question volume to automated AI quality scoring
Top Questions
See what users ask most. Sort by volume, failure rate, or feedback score. Understand the exact language your users use to find things.
Unanswered Questions
Questions BeforeQuery could not answer with confidence are flagged as knowledge gaps. These are your highest-priority docs improvements.
Feedback Scores
Users can rate any answer with a thumbs up or down. Negative feedback is surfaced with the original question and the answer that failed.
Daily Trends
Track conversation volume, feedback ratio, and unanswered rate day over day. Spot the impact of a docs update or a new product release instantly.
Latency Metrics
P50, P95, and P99 response times for retrieval and generation. Broken down by project, source type, and time period.
Groundedness Evals
Automated LLM-as-judge evaluation that scores each answer for faithfulness to retrieved sources. Catch hallucinations before users do.
Citation Quality
Evaluates whether cited sources actually support the answer. Low citation quality scores indicate retrieval returning irrelevant chunks.
Automated quality scoring on every answer
BeforeQuery runs LLM-as-judge evals asynchronously after each response. Groundedness score measures whether the answer stays within the retrieved context. Citation quality score measures whether the cited sources actually support each claim. Both scores trend over time so you can see the impact of docs improvements.
- Groundedness: answer vs. retrieved context faithfulness
- Citation quality: cited sources vs. answer claims
- Completeness: does the answer address the full question
- Eval scores exportable via API for custom dashboards
Frequently Asked Questions
Common questions about BeforeQuery Analytics
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