retrieval-augmented generation bolts a search engine onto a language model: fetch relevant documents at query time and paste them into the prompt, so the model answers from evidence rather than from its frozen weights (lewis et al. 2020, retrieval-augmented generation for knowledge-intensive nlp). 𐃏 it is the pragmatic alternative to baking every fact into an llm’s weights.
why retrieve at all
three problems that no amount of scaling fixes cleanly, and retrieval fixes cheaply: