RAG
Retrieval Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities.
It modifies interactions with a Large Language Model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.
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Tutorial
RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models
RAG integrates external knowledge retrieval with generative capabilities, enabling models to access up-to-date or domain-specific information.
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