From basic RAG to advanced recovery: A practical roadmap using the modern RAG stack

Author(s): Experience

Originally published on Towards AI.

Build intelligent, adaptive AI that understands and uses all your data sources

General-purpose LLMs are incredible, but they have one fundamental blind-spot: your data. Their knowledge was frozen at a specific point in time, and they have no knowledge of your company’s internal documents, your customer support tickets, or the data in your production database. And we can’t stick all of our company knowledge into a single prompt. This is the same problem that Retrieval-Augmented Generation (RAG) was created to solve.

This article provides a comprehensive roadmap for developing a Retrieval-Augmented Generation (RAG) system, discussing its initial setup and development into a production-ready system. It covers essential components such as effective indexing, query formation and intelligent routing as well as the importance of continuous assessment and observation to enhance system performance. By addressing common challenges such as refining retrieval quality and ensuring context-based generation, this piece aims to guide developers in building advanced AI systems that effectively leverage real-time data.

Read the entire blog for free on Medium.

Published via Towards AI