Retrieval-Augmented Generation first performs an information-retrieval step, searching a knowledge base or vector store for documents that relate to the user's query. The retrieved passages are then appended to the model prompt so the foundation model can generate a response grounded in that external, up-to-date content. The approach does not retrain the model, compress data into embeddings for faster inference, or create synthetic training sets; those are separate techniques.
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Applications of Foundation Models
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