Artificial Relevance

Artificial relevance describes the gap between what an AI retrieval system considers a match and what the user actually needs. It occurs when a system returns results that are semantically adjacent to the query - they share vocabulary, topic, or embedding proximity - but miss the specific intent behind the question. The results look relevant. They satisfy the algorithm. They fail the human.

The problem is most visible in vector-based retrieval systems. When a user asks "what was Q3 revenue for the European division," a vector search might return every paragraph that mentions revenue, Q3, or Europe, because those terms produce high cosine similarity scores. The system cannot distinguish between a paragraph that answers the question and a paragraph that merely discusses similar concepts. The mathematical distance is small. The informational distance is enormous.

Artificial relevance is a structural consequence of how embedding-based search works. Embeddings compress meaning into fixed-dimensional vectors, and similarity search finds the nearest points in that space. But proximity in embedding space does not guarantee relevance to a specific query in a specific context. A financial report section about revenue forecasts and a section about actual revenue figures may sit close together in vector space while serving completely different informational needs.

The concept has become increasingly important as retrieval-augmented generation systems are deployed in high-stakes domains like finance, law, and healthcare. When a RAG system feeds artificially relevant chunks to a language model, the model generates confident answers built on the wrong foundation. The output reads well, cites sources, and is wrong. This failure mode is harder to detect than a hallucination because the retrieved context genuinely exists in the source documents - it is just the wrong context for the question being asked. Approaches like tree-based document navigation, hybrid retrieval, and reasoning-based search are emerging specifically to address this limitation.