Building and Evaluating End-to-End RAG System Part - 2

Join us for an exclusive two-part webinar series that will revolutionize your approach to Retrieval-Augmented Generation (RAG) systems. Whether you're a seasoned AI professional or an enthusiastic newcomer, this comprehensive exploration of Building and Evaluating an End-to-End RAG system will equip you with cutting-edge knowledge and practical skills.

In the first session, we covered the fundamentals of RAG technology, including innovative data ingestion techniques, state-of-the-art text embedding strategies, and cutting-edge retrieval methods. Participants learned to craft robust RAG pipelines, leverage vector databases for optimal performance, and implement advanced similarity search. We also explored reranking methods, RAG advancements, and the implementation of open-source and LLM-based evaluations.

In this second session, the focus will shift to more advanced aspects of RAG. We will examine recent developments in the field, strategies for handling poor retrieval, and methods for efficiently evaluating RAG outputs. Furthermore, we will discuss the potential of using long-context LLMs, analyze alternative or open-source LLMs, and provide insights into how these can enhance RAG systems.