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

https://www.linkedin.com/in/vikram-chatterji/

Prior to Galileo, Vikram led Product Management at Google AI, where his team leveraged language models to build models for the Fortune 2000 across retail, financial services, healthcare and contact centers. He also led Product for Google Pay in India, taking it from 0 to 100M monthly users -- the most downloaded fintech app globally. Vikram was one of the early members of the Android OS team, and believes GenAI is ushering in a similar technological wave to mobile.

Atindriyo Sanyal Co-Founder and CTO

https://www.linkedin.com/in/atinsanyal/

Prior to Galileo, Atindriyo was an Engineering Leader at Uber AI, responsible for various Machine Learning initiatives at the company. He was one of the architects of the world's first Features store (Michelangelo) and early engineers on Siri at Apple, building their foundational technology and infrastructure that democratized ML at Apple.

Turbocharge Your RAG Applications with Powerful RAG Analytics

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