New technique makes RAG systems much better at retrieving the right documents

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Retrieval-augmented generation (RAG) has become a popular method for grounding large language models (LLMs) in external knowledge. RAG systems typically use an embedding model to encode documents in a knowledge corpus and select those that are most relevant to the user’s query.

However, standard retrieval methods often fail to account for context-specific de...

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