How do you handle knowledge conflicts in Agentic RAG wh...

Retrieved chunks contradicting each other is a routine occurrence once a knowledge base spans multiple document versions, authors, or update cycles — an older doc quoting one price next to a newer doc quoting another, or an informal FAQ disagreeing with the official policy it’s meant to summarize. Left unhandled, the model tends to blend these into a single confident-sounding answer that quietly picks one source or, worse, mixes numbers from both.

The fix is to make conflict detection an explicit step rather than an implicit hope: ask the model directly whether the retrieved chunks disagree before generating, then resolve using clear signals — prefer the more recent document, weight official sources above unofficial ones, and for genuinely ambiguous or high-stakes conflicts (medical, legal, pricing disputes), either present both positions with attribution to the user or escalate to a human rather than guess. Being transparent that a conflict exists is almost always safer than resolving it silently and being wrong.