“AI summarized 100 user tickets into a neat report - how do you verify it didn't invent trends?”
Filter out hallucinations and over-extrapolations in AI feedback summaries
Recommended AI Agent Skills
Citation-verified web & user feedback data extraction with exact source attribution.
CitationsVerificationData Extraction
Forces a verification pass before declaring anything "done" - cross-checking every claim against its original source instead of trusting the summary.
VerificationQualityAnti-hallucination
Synthesizes user feedback into themes with real mention counts, preventing the AI from inflating a single comment into a false trend.
Feedback SynthesisProduct Management
Action framework
- Require the AI to attach direct ticket IDs or exact verbatim quotes to every listed insight.
- Spot-check 5 random citations against raw logs - if even one is fabricated, reject the summary.
- Force the AI to output exact mention counts rather than vague qualifiers like 'most users'.
- Compare summary points against lowest-rated (1-star) feedback to ensure critical negative signals were not smoothed away.
- Retain only insights backed by at least 3 distinct, independent feedback instances.
Before
"The AI summary reads smoothly and makes sense, forward it straight to leadership."
After
"Caught AI fabricating a 'huge demand for dark mode' from a single mention. Enforced ticket citations and counts: only 3 issues with >10 occurrences made the roadmap."