Overview:

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

A key focus was on the data that powers RAG—how raw, unstructured information needed to be molded, cleaned, and structured so it could meaningfully serve downstream AI workflows. We explored how data strategies should evolve alongside RAG strategies, and we closed with a live demo showing how Unstructured helped transform messy data into RAG-ready context.

Technical Details:

In this session, we walked 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