EVALS & QUALITY
Measure answer quality, don't assume it
An assistant you can't measure is an assistant you can't trust in production. BeforeQuery scores answers with LLM-judge evals, collects user feedback, records full traces, and turns weak spots into a concrete content backlog.
The quality loop
Score, inspect, fix, repeat
LLM-judge scoring
Automated Evals
- Answers scored by an LLM judge per knowledge base
- Quality tracked over time as content changes
- Catch regressions after large source updates
- Eval scores exposed in the dashboard and API
- No hand-built test harness required
Users vote
Human Signals
- Per-message feedback across widget, bots, and portal
- Feedback aggregated into quality reporting
- Simulations replay historical tickets before launch
- Human review queues for simulation results
- Deflection counted only for grounded answers
See inside any answer
Deep Inspection
- Traces for chat, search, MCP, A2A, and deflection
- Retrieved chunks and scores per conversation
- Token and cost analytics per knowledge base
- Knowledge-gap report of weak and missing topics
- Doc proposals draft the fixes
Close the loop
From weak answer to fixed doc
Quality problems in RAG are almost always content problems. BeforeQuery connects the measurement to the fix: evals and feedback find the weak answers, traces show why retrieval missed, gap analytics rank what's missing, and doc proposals draft the missing page — turning quality management into an editorial workflow.
- Eval scores identify weak topics per knowledge base
- Traces reveal whether retrieval or content was at fault
- Gap report ranks missing knowledge by question volume
- Doc proposals generate drafts your team reviews and publishes
- Re-run evals to confirm the fix moved the score
Frequently Asked Questions
Common questions about Evals & Answer Quality
An evaluation model scores answers for groundedness and quality against the retrieved sources, per knowledge base. Scores accumulate over time so you can see whether a big docs migration or source change helped or hurt.
Yes. Simulations replay historical helpdesk tickets through the assistant and produce reviewable results with a human sign-off step — so you know deflection behavior before enabling it.
Each trace records the question, the retrieved chunks with scores, and the generated answer — across chat, search, MCP, A2A, deflection, and agent runs. When an answer is wrong, the trace shows whether retrieval missed or the content was missing.
Gap analytics rank unanswered and low-confidence topics by volume, and doc-proposal scans draft candidate pages for those gaps. Your team accepts or dismisses each draft — the AI proposes, humans publish.
Yes. Token and cost analytics are reported alongside gaps per knowledge base, and workspace usage metering tracks resolved-versus-deflected outcomes.
Run AI answers like a product
Measure quality from day one — evals, feedback, and traces are built in.
Get Started Free