RAG (Retrieval Augmented Generation)

A model technique that retrieves data from external sources to improve response accuracy.

What is RAG (Retrieval Augmented Generation)?

RAG combines information retrieval with text generation to produce more accurate and factual responses. In AI automation, RAG enables models to incorporate specific, relevant information from trusted sources.

RAG capabilities allow for the creation of workflows that can access and utilize specific knowledge bases or documents while maintaining accuracy.

Why is RAG (Retrieval Augmented Generation) important?

RAG significantly improves the accuracy and reliability of AI-generated content. For businesses using Lleverage, this technology enables the creation of automation workflows that can leverage their specific knowledge bases and documentation. This leads to more accurate and contextually relevant automated responses.

How you can use
RAG (Retrieval Augmented Generation)
with Lleverage

A technical support team uses Lleverage to automate response generation. Their workflow uses RAG to retrieve information from product documentation and support histories, ensuring responses are accurate and consistent with the latest product information.

RAG (Retrieval Augmented Generation)
FAQs

Everything you want to know about

RAG (Retrieval Augmented Generation)

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How does RAG reduce AI hallucinations?

By grounding responses in retrieved information rather than relying solely on model knowledge.

What kinds of data sources work best with RAG?

Structured documentation, knowledge bases, and other authoritative sources that can be easily indexed and retrieved.

More references for
RAG (Retrieval Augmented Generation)

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