Overview:

RAG (Retrieval-Augmented Generation) has become a cornerstone of modern AI systems—but not all RAG strategies are created equal. In this session, we’ll walk through how to think about different RAG approaches, starting from vanilla retrieval setups to more advanced, modular systems that can adapt to your specific use cases. We’ll break down complex-sounding terms into simple building blocks and show how these “Lego pieces” can be combined to meet your application’s needs.

A key focus will be on the data that powers RAG—how your raw, unstructured information needs to be molded, cleaned, and structured so it can meaningfully serve your downstream AI workflows. We’ll explore how your data strategy should evolve alongside your RAG strategy, and we’ll close with a live demo showing how Unstructured can help transform messy data into RAG-ready context.

Technical Details:

In this session, we’ll walk through:

• Understanding vanilla RAG and common extensions (multi-vector, hybrid, and agentic retrieval)
• Evaluating tradeoffs between retrieval strategies (speed, precision, scalability)
• How unstructured data quality directly impacts RAG performance
• Preparing and transforming data for retrieval pipelines using Unstructured

This session includes a practical walkthrough and live Q&A. Can’t make it live? Register anyway and we’ll send you the recording.