Retrieval Augmented Generation (RAG) is a method for enhancing Large Language Models with accurate, real‑world information. By grounding responses in retrieved data, RAG reduces hallucinations and enables the model to act as an expert in any domain. In this approach, InterSystems IRIS Vector Search identifies the most relevant content from the database. That information is then combined with the user’s prompt and sent to a large language model to generate a more accurate, context‑aware response.

In this interactive tutorial, you will learn how to:

• Understand the fundamentals of Retrieval-Augmented Generation (RAG)
• Use InterSystems IRIS Vector Search to store and query vector embeddings
• Combine vector search results with a generative AI model
• Build a simple AI chatbot that provides context-aware, accurate responses
• Integrate InterSystems IRIS with Python application frameworks using SQLAlchemy and LangChain

This tutorial is ideal for:

• Developers interested in building AI-powered applications using RAG (Retrieval-Augmented Generation)
• Developers exploring vector search and semantic retrieval
• Engineers who want a quick, hands-on introduction to InterSystems IRIS Vector Search

No prior experience with vector databases or RAG is required.

• Basic understanding of Python coding
• Familiarity with general AI or LLM concepts is helpful but not required
• A modern web browser
• No local installation or configuration needed

After completing this tutorial, you will have:

• A practical understanding of how RAG works in real applications
• Hands-on experience using InterSystems IRIS Vector Search
• A working example of an AI chatbot enhanced with retrieval capabilities