Retrieval Augmented Generation: Difference between revisions
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{{GlossaryEntry | {{GlossaryEntry | ||
|description=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. This allows LLMs to use domain-specific and/or updated information.[1] Use cases include providing chatbot access to internal company data, or giving factual information only from an authoritative source.[2] | |||
|references=https://en.wikipedia.org/wiki/Retrieval-augmented_generation | |references=https://en.wikipedia.org/wiki/Retrieval-augmented_generation | ||
|lang=en | |lang=en | ||
Revision as of 15:01, 10 November 2024
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| description | 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. This allows LLMs to use domain-specific and/or updated information.[1] Use cases include providing chatbot access to internal company data, or giving factual information only from an authoritative source.[2] |
| references | https://en.wikipedia.org/wiki/Retrieval-augmented_generation |
| lang | en |
| master | |