In this in-depth episode, Robert and Haley continue their journey through the world of Retrieval-Augmented Generation (RAG) systems. This is Part 2 of our crash course on RAG, where we explore the nuts and bolts of evaluating these powerful information-synthesis tools. With implementations and metrics as our focus, we’ll share key insights on how to assess and refine RAG systems, ensuring accuracy, context, and efficiency in real-world applications.

Here’s what we’ll cover:

Essential RAG Components: From chunking data and embedding models to storage and vector retrieval
Evaluation Metrics: The must-track factors to maintain RAG accuracy and relevance
Practical Implementation Tips: Streamlining RAG workflows for applications like customer support, research, and AI-driven synthesis

Whether you’re a data science enthusiast, developer, or simply curious about how AI systems retrieve and generate answers, Robert and Haley break down the essentials for building and perfecting RAG systems. Don’t miss this episode!

Demystifying RAG Systems: Building and Evaluating Retrieval-Augmented Generation 13:34 Share

The Quantum Drift

Shownotes Transcript

Demystifying RAG Systems: Building and Evaluating Retrieval-Augmented Generation