Build A Database RAG AI Assistant with OPENAI and LangChain

About this course

Artificial Intelligence is rapidly transforming how we interact with data. Instead of writing complex SQL queries, imagine asking questions in plain English and having an intelligent assistant retrieve the information instantly from a database.

In this hands-on course, you will learn how to build a complete AI-powered Database RAG (Retrieval-Augmented Generation) Assistant using Python, OpenAI, LangChain, and SQL Server.

Rather than focusing only on theory, this course guides you step-by-step through building a real-world AI application that can understand natural language questions, generate SQL queries automatically, retrieve results from a database, and explain the results in simple human-readable language.

By the end of the course, you will have built a fully functional AI data assistant that allows users to interact with databases conversationally.

Throughout the course, you will learn how modern AI systems combine large language models with structured data sources using Retrieval-Augmented Generation (RAG). You will also discover how tools like LangChain help orchestrate AI workflows and how OpenAI models can transform natural language questions into database queries.

We will also build a user-friendly interface using Streamlit, allowing users to explore database schemas, generate queries, run analysis, and ask follow-up questions about the data.

To ensure the system is safe and production-ready, the course also demonstrates how to implement AI guardrails that prevent destructive database operations and restrict queries to safe read-only operations.

This project-based course is ideal for developers, data professionals, and AI enthusiasts who want to learn how modern AI assistants and data copilots are built.

By completing this course, you will gain practical experience with:

• Retriev

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