What is a cross-source contradiction in AI?

A cross-source contradiction happens when two documents in your knowledge base make conflicting claims about the same thing — usually an old version and its replacement both sitting in the index. Standard RAG retrieves one and answers from it without flagging that the other exists.

Why doesn't standard RAG catch contradictions?

Top-k retrieval is built to pick winners, not to notice conflicts. It ranks the most relevant chunks and passes them to the model. If two retrieved chunks disagree, nothing in the pipeline raises a flag — the model silently resolves the conflict and produces a confident, cited answer.

How do you resolve conflicting AI sources?

Surface the contradiction instead of hiding it: show both versions, each one's last-verified timestamp, and a confidence score, then route the conflict to the source-of-truth owner before the output ships. Every override is logged for the audit trail.

When Your Sources Disagree: How Silent Contradictions Poison AI Answers

Two internal documents describe the same policy differently. One is current; one is a year old and was never retired. Both are sitting in your vector store. A user asks the question, retrieval pulls them both, and your AI answers — picking one, silently. Which one did it use? You don’t know. Neither does the user. The answer comes back confident and cited.

That’s a cross-source contradiction, and it’s one of the quietest ways a knowledge base poisons AI output.

Why contradictions are invisible

Top-k retrieval is designed to pick winners. It scores chunks by relevance and hands the best ones to the model. Nothing in that pipeline is designed to notice that two of those chunks disagree. The model receives conflicting context and does what models do — it resolves the conflict internally and produces fluent text, with no signal that a conflict ever existed.

So the failure is doubly hidden: the contradiction is invisible to the system, and the resolution is invisible to you. You get a clean, sourced answer that happens to be wrong half the time.

The cost of silent resolution

A hallucination at least looks suspicious on inspection. A silently-resolved contradiction looks perfect — it cites a real document and reads with total confidence. In a support context that’s a wrong answer to a customer. In a regulated context it’s a decision you can’t defend, because you can’t even say which of two conflicting sources drove it.

This is the same family of problem as knowledge decay: the source is real and retrieved correctly, but the ground truth is broken. Decay is one source going stale; contradiction is two sources disagreeing and no one refereeing.

Surface the conflict instead of hiding it

The fix isn’t a smarter model — it’s making the conflict visible before the answer ships:

Detect the contradiction. Flag when two trusted sources make incompatible claims, using metadata-only signals so no document content leaves your perimeter.
Show both sides. Present both versions, each one’s last-verified timestamp, and a confidence score — the raw material a human needs to adjudicate.
Route to the owner. Send the conflict to the source-of-truth owner with both versions and the conflict timeline, rather than letting the model guess.
Enforce a policy. Configurable thresholds let you block, warn, or annotate any answer that depends on a contradicted or stale source — different rules for different products and customer tiers.
Log the resolution. Every override is recorded, so the audit trail shows not just what the AI answered but how a conflict was settled and by whom.

This is what knowledge-integrity controls do that detection alone can’t: they put a human in the loop before the output reaches a customer or a regulator, and they leave a record afterward.

Provenance is the prerequisite

You can only flag a contradiction if you’re already tracking each source’s version and verification state — which is exactly what continuous provenance gives you. Contradiction detection isn’t a bolt-on feature; it falls out naturally once every retrieval carries a real provenance record.

Where to start

Most teams have no idea how many contradictions are already live in their knowledge base. Join the waitlist for a free Provenance Audit to see the contradictions and stale-source candidates in one knowledge base — before a customer finds them for you.

ProvenanceOps is part of the AI Vyuh AI-operations stack — the layer that proves what your agents know is still true.