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Tweak USPs
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leemthompo committed Jan 7, 2025
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Expand Up @@ -14,18 +14,18 @@ Compared to fine-tuning or continuous pre-training, RAG can be implemented more
image::images/search/rag-venn-diagram.svg[RAG sits at the intersection of information retrieval and generative AI, align=center, width=500]

RAG sits at the intersection of https://www.elastic.co/what-is/information-retrieval[information retrieval] and generative AI.
{es} is an excellent tool for implementing RAG, because it offers various retrieval capabilities, such as full-text search, vector search, and hybrid search.
{es} is an excellent tool for implementing RAG, because it offers various retrieval capabilities, such as full-text search, vector search, and hybrid search, as well as other tools like filtering, aggregations, and security features.

[discrete]
[[rag-elasticsearch-advantages]]
=== Advantages of RAG

RAG has several advantages:
Implementing RAG with {es} has several advantages:

* *Improved context:* Enables grounding the language model with additional, up-to-date, and/or private data.
* *Reduced hallucination:* Helps minimize factual errors by enabling models to cite authoritative sources.
* *Cost efficiency:* Requires less maintenance compared to fine-tuning or continuously pre-training models.
* *Enhanced security:* Controls data access by leveraging {es}'s <<authorization, user authorization>> features, such as role-based access control and field/document-level security.
* *Built-in security:* Controls data access by leveraging {es}'s <<authorization, user authorization>> features, such as role-based access control and field/document-level security.
* *Simplified response parsing:* Eliminates the need for custom parsing logic by letting the language model handle parsing {es} responses and formatting the retrieved context.
* *Flexible implementation:* Works with basic <<full-text-search,full-text search>>, and can be gradually updated to add more advanced and computationally intensive <<semantic-search,semantic search>> capabilities.

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