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An RAG system links an AI to trusted documents to produce useful, sourcing and rights-of-access responses.
The RAG, for Retrieval- Augmented Generation, allows an AI assistant to search a documentary database before formulating his answer. Its value depends primarily on the quality of the information system it uses.
An RAG does not correct obsolete or contradictory documentation. Reference sources should be identified, their updating cycles should be understood, and content that should not be interviewed should be excluded.
The answer is good if the right extracts are found. Document cutting, metadata, search engine and rights filters are therefore structuring choices. AI should not respond with confidence when the source is missing.
Displaying the documents used, the passages cited and the level of trust helps the user to verify the information. This is particularly important when the RAG assists a business team, support, quality or operations.
APPLIED AI
Computer vision, YOLO and RAG become useful when they connect to your real data, teams and constraints.
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