Difference between revisions of "Retrieval Augmented Generation"

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{{GlossaryEntry
 
{{GlossaryEntry
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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. Use cases include providing chatbot access to internal company data, or giving factual information only from an authoritative source.
 
|references=https://en.wikipedia.org/wiki/Retrieval-augmented_generation
 
|references=https://en.wikipedia.org/wiki/Retrieval-augmented_generation
 
|lang=en
 
|lang=en
 
}}
 
}}
=Freitext=
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= Links =
 
= Links =
 
[https://weaviate.io/blog/introduction-to-rag?utm_source=newsletter.weaviate.io&utm_medium=referral&utm_campaign=introducing-european-hack-nights-compound-ai-systems-and-ai-in-education Weaviate blog entry on RAG]
 
[https://weaviate.io/blog/introduction-to-rag?utm_source=newsletter.weaviate.io&utm_medium=referral&utm_campaign=introducing-european-hack-nights-compound-ai-systems-and-ai-in-education Weaviate blog entry on RAG]

Latest revision as of 16:03, 10 November 2024

GlossaryEntry

GlossaryEntry
responsible  
state  
since  
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. Use cases include providing chatbot access to internal company data, or giving factual information only from an authoritative source.
references  https://en.wikipedia.org/wiki/Retrieval-augmented_generation
lang  en
master  

Links

Weaviate blog entry on RAG