landing_page/qdrant-landing/content/retrieval-augmented-generation/rag-evaluation-guide-integrations.md

title, description, cards, frameworksTitle, frameworksDescription, frameworksCards, sitemapExclude

title	description	cards	frameworksTitle	frameworksDescription	frameworksCards	sitemapExclude
Why evaluate your RAG application?	The guide will outline both common issues, as well as recommendations to avoid these pitfalls.	id image description 0 src alt /img/rag-evaluation-guide/integrations/maximize-search.svg Maximize search Lack of Precision id image description 1 src alt /img/rag-evaluation-guide/integrations/enrich-context.svg Enrich context Poor recall id image description 2 src alt /img/rag-evaluation-guide/integrations/avoid-hallucinations.svg Avoid hallucinations “Lost in the middle”	Recommended evaluation frameworks	In the guide, we explore three popular frameworks that can help simplify your evaluation process.	id image description 0 src alt /img/rag-evaluation-guide/integrations/ragas.svg Ragas logo Ragas is an open-source framework for evaluating retrieval augmented generation systems. id image description 1 src alt /img/rag-evaluation-guide/integrations/quotient.svg Quotient AI logo Quotient AI is a platform that focuses on building and deploying RAG systems. id image description 2 src alt /img/rag-evaluation-guide/integrations/arize.svg Arize logo Arize Phoenix is a tool designed for monitoring and observability in AI systems, including RAG pipelines.	true