Chat with Your Data Using ChatGPT

Discover how to build an intelligent agent that can retrieve, reason, and remember. In this hands-on course, you will create an agentic RAG (retrieval-augmented generation) system that connects a language model to real-world data to enable dynamic question answering and discovery. You will start by setting up your development environment and preparing a dataset, then build an agent capable of autonomous reasoning, retrieval, and long-term memory. Along the way, you will extend your agent with custom tools for translation, analysis, and pattern detection to transform it into a research assistant. By the end of the course, you will have a working interactive agent that can chat with your data and a clear blueprint for adapting agentic RAG systems to real-world use cases.

Learning objectives
Build an intelligent AI agent that can reason, plan, and act autonomously.
Implement an agentic RAG (retrieval-augmented generation) workflow that retrieves, analyzes, and summarizes information from a knowledge base.
Integrate long-term memory and multi-step reasoning loops so your agent can learn and improve across sessions.
Develop custom agent tools, such as translation or pattern analysis modules, to extend your agent’s domain expertise.
Deploy your agentic RAG system as an interactive chat experience and adapt it for real-world use cases like research assistants, compliance bots, or customer support AIs.