Future of LLM Applications with LangChain and MongoDB

In today's rapidly evolving AI landscape, retrieval-augmented generation (RAG) is becoming essential for enterprises that want to build scalable, reliable AI solutions.

This insightful webinar explores the future of large language model (LLM) applications. We explore effective strategies for implementing AI agents and RAG using LangChain and MongoDB and gain practical solutions to overcome common challenges in developing LLM applications.

LangChain provides a powerful framework to build, run, and manage LLM applications, while MongoDB Atlas’ vector database unifies operational, metadata and vector embeddings to build intelligent solutions. The combination of these powerful tools enable enterprises to securely launch gen AI applications to production.

Prakul Agarwal, Sr. Product Manager at MongoDB, and Erick Friis, Founding Engineer at LangChain, share insights into the deep integration between MongoDB Atlas and LangChain to help you:

• Develop powerful AI agents.
• Conduct RAG and address ingestion challenges.
• Build Agentic RAG using MongoDB as a checkpointer.
• Progress from simple to advanced retrieval.

Don’t miss this opportunity to take your gen AI applications to the next level.