Retrieval-Augmented Generation

What it is

Retrieval-augmented generation searches a document set for relevant passages and gives them to a language model as the basis for its answer. Introduced by Lewis and colleagues in 2020, it separates what a system knows from what it was trained on, which is why it became the default pattern for company-specific answers.

Why it matters

RAG relocates the accuracy problem rather than solving it. The model is now only as reliable as the passages retrieved, and those are only as reliable as the corpus they came from — so an unreconciled document set produces an unreliable system no matter how good the retrieval or the model. Teams routinely tune chunking and rerankers for months while the underlying corpus still contains three different answers to the same question.