RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) lets a language model generate an answer based on documents retrieved at the moment of the question (internal documentation, knowledge base, product catalog), rather than relying only on its general training knowledge. The question is used to search for relevant passages, which are then passed to the model as context.
This is the technique to reach for whenever an AI assistant needs to answer with information specific to a company and updated regularly: pricing terms, internal procedures, product sheets. An internal support tool, for instance, can answer using the actual technical documentation, refreshed every week, without ever retraining a model. It's one of the most requested generative AI projects in business settings.
RAG quality depends almost entirely on the retrieval step upstream, not on the language model chosen. A document base poorly split into chunks, or a search that fails to surface the right passages, produces wrong answers even with a strong model. Chunking and indexing the documents deserve as much attention as picking the model.
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