---
title: RAG Evaluation
descriptionFirstPart: Retrieval Augmented Generation (RAG) harnesses large language models to enhance content generation by effectively leveraging existing information. By amalgamating specific details from various sources, RAG facilitates accurate and relevant query results, making it invaluable across domains such as medical, finance, and academia for content creation, Q&A applications, and information synthesis.
descriptionSecondPart: However, evaluating RAG systems is essential to refine and optimize their performance, ensuring alignment with user expectations and validating their functionality.
image:
  src: /img/retrieval-augmented-generation-evaluation/become-a-partner-graphic.svg
  alt: Graphic
partnersTitle: "We work with the best in the industry on RAG evaluation:"
logos:
- id: 0
  icon:
    src: /img/retrieval-augmented-generation-evaluation/arize-logo.svg
    alt: Arize logo
- id: 1
  icon:
    src: /img/retrieval-augmented-generation-evaluation/ragas-logo.svg
    alt: Ragas logo
- id: 2
  icon:
    src: /img/retrieval-augmented-generation-evaluation/quotient-logo.svg
    alt: Quotient logo
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---

