AI trust question
RAG failures are usually pipeline failures, not one bug. Retrieval, ranking, context assembly, and generation can each add subtle errors that look reliable in the final output. In practice, teams add an audit step before release to catch these gaps before users rely on them.
RAG output can still be wrong when retrieval is stale, incomplete, or overextended during generation.
A retrieved source can be relevant but outdated, partial, or missing a crucial caveat. The answer then inherits those gaps while still sounding definitive.
This is why source presence alone is not enough. You need source quality, recency, and fit to the exact user question.
Even with good context, generation can merge sources incorrectly or extrapolate beyond what the documents justify.
That overreach is especially risky when the response uses confident language and users treat it as final guidance.
Check claim-to-source alignment for high-impact claims, then route uncertain outputs through review before release.
A trust decision layer helps catch subtle contradictions and unsupported extensions that retrieval metrics alone will miss.
A RAG answer cites policy text from last year while recommending a workflow removed in the current revision.
Next step: Route to review, refresh retrieval context, and regenerate against current policy sources.
RAG reliability checks should confirm that generated claims align with retrieved sources and current context.
LLM applications leak unsafe output when validation is optional instead of enforced before delivery.