In this unit of our LLM Observability Certification Series, we will cover Search & Retrieval methods for LLMs like Retrieval Augmented Generation (RAG). Through pre-recorded instructor videos, checkpoint questions, and unit labs, you will learn how implementing RAG in your LLM application system can provide performance optimization for your LLM. In addition to learning how to provide your LLM with a relevant knowledge base (outside of its training data sources and before generating a response) you will also learn how to evaluate a RAG system. You will do this is by applying AI Observability via Evaluation Traces and Spans via Arize's open-source solution – Phoenix.

Format and duration: Self-Paced, 2 hours

Upon completion, you will receive a unit certificate acknowledging your new skills!