Turbocharge Your RAG Applications with Powerful RAG Analytics
RAG (Retrieval-Augmented Generation) has emerged as a leading approach in developing generative AI applications. However, building error-free RAG systems comes with significant challenges, including maintaining chunk and document quality, refining prompts, and addressing output hallucinations.
In this session, we'll distill our learnings from conversations with hundreds of AI teams utilizing RAG. Our focus will include:
Overcoming the 5 major roadblocks in RAG application development
Effective strategies for mitigating these 5 challenges
Introducing 4 robust evaluation metrics to identify and rectify issues in your RAG system
How to build production-ready RAG-powered applications 10x faster
About DeepLearning.AI:
DeepLearning.AI is an education technology company that is empowering the global workforce to build an AI-powered future through world-class education, hands-on training, and a collaborative community. Take your generative AI skills to the next level with short courses help you learn new skills, tools, and concepts efficiently.
About Galileo:
Galileo is a GenAI Evaluation and Observability solution that helps enterprise teams bring production-ready applications to market faster. Galileo instantly integrates into any AI environment and enhances critical workflows with collaborative tools and research-backed evaluation metrics. Today, Galileo is trusted by AI builders from cutting-edge startups to Fortune 500 companies.
Vikram Chatterji Co-Founder and CEO
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Atindriyo Sanyal Co-Founder and CTO
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