From basic RAG to advanced search: a practical roadmap using the modern RAG stack

Author's): Anubhav

Originally published in Towards Artificial Intelligence.

Create intelligent, adaptive AI that understands and leverages all your data sources

General-purpose LLMs are amazing, but they have a fundamental weakness: your data. Their knowledge is frozen at a specific point in time and they have no idea about your company's internal documents, customer service tickets, or data in your production database. We can't just paste our entire company's knowledge into one prompt. This is exactly the problem that recovery assisted recovery (RAG) technology was created to solve.

This article presents a comprehensive development plan for a search augmented generation (RAG) system, discussing its initial configuration and evolution into a production-ready system. It covers core components such as efficient indexing, query construction, and intelligent routing, as well as the importance of continuous evaluation and observability to improve system performance. By addressing common challenges such as improving search quality and ensuring context-aware generation, the article aims to help developers build advanced AI systems that effectively leverage real-time data.

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Published via Towards AI