Building Robust RAG Applications Using LangChain & LlamaIndex

In this session, participants will gain a comprehensive understanding of Retrieval-Augmented Generation (RAG): its core principles, operational mechanics, and the critical need it addresses in enhancing Large Language Model (LLM) capabilities. We will delve into the practical aspects of constructing resilient RAG systems by exploring two prominent LLM frameworks: LangChain and LlamaIndex. Through comparative analysis, attendees will learn the strengths and unique features of each framework in the context of RAG implementation. The session will culminate in a hands-on tutorial demonstrating the step-by-step process of building functional RAG pipelines using both LangChain and LlamaIndex, providing practical insights into their application.

Key Takeaways:

• Participants will gain a comprehensive understanding of Retrieval-Augmented Generation (RAG), including its core principles, operational mechanics, and how it significantly enhances Large Language Model (LLM) capabilities.
• The session will provide a comparative analysis of LangChain and LlamaIndex, enabling attendees to understand the unique strengths and features of each framework in the context of RAG implementation.
• Attendees will learn the practical aspects of constructing resilient RAG systems, focusing on best practices for building robust applications.
• The workshop culminates in a hands-on tutorial demonstrating the step-by-step process of building functional RAG pipelines using both LangChain and LlamaIndex, offering valuable practical insights.

Pavan is a technology evangelist and an author. He has written over 100 articles on AI and other technical topics on various tech publications.

He is also a speaker who talks regularly on the topics related to GenAI. He is a LinkedIn top voice and currently working at SingleStore as a technology evangelist.