AI Expert

Guides/Hallucination & grounding

Hallucination & grounding playbook

Confident wrong answers erode trust faster than slow answers. Product teams ship resilience by pairing retrieval + citation UX when facts matter, calibrated hedging when evidence is thin, and explicit abstention when stakes exceed model proof.

Response posture flow

Use with legal/clinical partners — this diagram encodes product judgment, not a regulatory framework by itself.

Choosing citations, hedging, abstention, or escalation by question stakesAnswer could harmif wrong?Verifiable fromtrusted corpus?YesNo / partialRetrieve + citeShow sources in UXHedge / abstainOffer escalation pathLow stakesBrainstorm /draft modeDisclaim; skip fake citationsPolicy triggers (regulated domains) →forced human review regardless of UI pathTie grounding to eval suites — spot-checkabstention rates like toxicity checks.
Engineers implement retrieval + citation plumbing; PMs own when abstention is preferable to fluent-but-wrong answers.

Patterns in practice

PatternUX surfaceTypical backend hooks
Citation-backed answerInline sources, “jump to doc” linksRAG with chunk attribution; freshness SLAs
Calibrated hedge“Based on limited info…” + what would verifyConfidence routing; optional second-pass retrieval
Hard abstainRefuse + suggest human / ticketClassifier or policy gate before generate
Creative draftMode badge; no fabricated citationsSeparate prompt path; disable retrieval hooks

Trust vs friction tradeoff

Strict grounding slows happy-path fluent demos — negotiate personas (support vs creative) instead of one-size defaults.

Trust vs product friction (schematic)

Higher friction includes loading sources, longer responses, extra taps — tune per persona.

Interview talking point: shipping fluent-default raises short-term delight and long-term incident risk.