Key takeaways

Vector search measures similarity; therefore, contradictory statements about the same subject can receive nearly identical scores.

Standard retrieval-augmented generation (RAG) techniques like chunking and top-k retrieval can strip away context or hide conflicting information from an AI agent.

Combining vector search with keyword search, metadata, conflict detection, and reranking can give agents the context they need to handle disagreements.

Imagine that a RAG system asks a vector database to retrieve the most relevant information about whether a product, process, or action is safe. It may return answers that both “X is safe” and “X is not safe” with almost the same similarity score. Better embedding models narrow the gap, but you generally can't tune it away with the embedding alone. It comes from what similarity search is built to do.

\r\n The problem is that similarity search is designed to find things that look alike — but opposites can look alike, too.…