Tagged information retrieval · show all articles
Retrieval-augmented generation fails quietly. Ten distinct, documented mechanisms, from a stale index to a fabricated citation, account for nearly every expensive production RAG incident, and each has its own detection and its own fix.
Two axes decide what retrieval-augmented generation becomes by 2035: whether evidence-gathering stays a separate, inspectable step, and whether what it returns can be verified. Crossing them gives four futures, and current evidence does not yet pick one.
Retrieval-augmented generation is usually described as one step: ask, then answer. In production it is six separate stages, each with its own literature, its own failure mode, and its own knob to turn.
Naive, hybrid, graph-augmented, iterative, and agentic retrieval are five distinct engineering systems, not five leaderboard entries. Compared on latency, cost, and failure surface, each wins a different workload — and none of them ranks the others.
Retrieval-augmented generation is usually dated to one 2020 paper. Its lineage is older: 1990s sparse ranking, retrieve-then-read question answering, dense encoders, and now retrieval that decides for itself when to search again.
A retrieval layer does not let a model remember. It submits evidence in support of an answer not yet written. Almost every expensive failure in production RAG follows from confusing the two.
An embedding is a filing decision, not a fact about meaning. Its geometry guarantees far less than the systems built on top of it assume, and every surprising retrieval failure follows from that gap.