RAG enables AI to answer from company documents without retraining

Retrieval-augmented generation (RAG) is a technique that allows AI models to answer questions based on a specific set of documents without retraining the model. This process involves a search engine identifying relevant passages from a document base and a language model then synthesizing an answer using those passages, crucially citing the sources. RAG systems require a preparation phase to convert, clean, chunk, and index documents into searchable vectors, followed by a retrieval and answer phase where the model uses the indexed information to respond to user queries. AI

IMPACT RAG enhances AI utility by enabling it to access and cite specific, private data without costly retraining.

RANK_REASON The item describes a technical approach (RAG) for AI, referencing a specific paper and its authors. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• French Directorate General for Enterprise
• Lewis et al.
• retrieval-augmented generation

• product
• infra

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