A news archive published over years inevitably contains contradictions: facts that were accurate when reported but have since changed, corrections that were applied to some articles but not to others, and evolving scientific or policy consensus that different articles represent differently. An AI-powered contradiction detection system can surface these inconsistencies for editorial resolution.
How to Build a Contradiction Detection System
A simple contradiction detector works as follows: for each new article published, extract all factual claims. Query the archive RAG for documents containing claims about the same entities (same persons, organisations, statistics). Flag pairs where the claims contradict (different values for the same statistic, different stated positions for the same person). Alert the editor to the specific contradiction for human resolution — either updating the older article or noting that the discrepancy represents genuine change over time. Commercial RAG systems (Pinecone, Weaviate) support this pattern with metadata filtering.