Building applications with Large Language Models (LLMs) that leverage agentic networks and Retrieval Augmented Generation (RAG) presents unique challenges for developers. Issues like retrieval accuracy, context adherence, and relevance can significantly impact the user experience. Additionally, the inability to trace the sequence of events leading to errors makes debugging and optimization difficult and time-consuming.

This webinar will delve into these common pain points and explore strategies to enhance the development process. We'll discuss techniques for improving retrieval accuracy and ensuring context adherence within the agentic network and RAG framework. Additionally, we'll examine methods for efficient error tracking and debugging, enabling developers to identify and resolve issues more effectively.