Retrieval augmented generation (RAG) combines retrieval-based and generative AI techniques for large language models (LLMs) to access and use relevant external information and produce accurate, context-aware responses. However, RAG’s effectiveness depends on retrieving the right context for each specific task.

In this tutorial, explore how to use IBM watsonx Orchestrate and DataStax Astra DB to build an agentic RAG system that delivers contextually relevant answers about HR employee benefits. The AI agent uses documents that are extracted from the website and stored in Astra DB. When the user asks a question, the AI agent reformulates the question to find the most relevant documents and uses that context to generate an accurate answer.

Link to Tutorial