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The Complete Guide

What is Retrieval Augmented Generation?

Retrieval-augmented generation (RAG) is a generative AI framework for improving the accuracy and reliability of large language models (LLMs), using relevant data from company sources.

Learn why providing reliable responses is not so easy, what are some of the challenges with this approach, and what are some of the RAG use cases you could start with.

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RAG K2view

Highlights:

  • Current state of retrieval-augmented generation (RAG)
  • RAG benefits and challenges 
  • The data retrieval process
  • RAG chatbot: A natural starting point

Grounding GenAI apps with enterprise data

A complete RAG implementation – one that retrieves structured data from enterprise systems, as well as unstructured data from knowledge bases – can transform real-time, multi-source business data into intelligent, context-aware, and compliant prompts to reduce GenAI hallucinations and elevate the effectiveness and trust of GenAI apps.

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