Chatbots powered by Large Language Models (LLMs) are impressive — but without grounding in real data, they often make things up. Retrieval-Augmented Generation (RAG) provides a solution by combining LLMs with vector databases and search pipelines. This session will walk attendees through designing and deploying a practical RAG chatbot that can answer domain-specific questions using private datasets.

What attendees will learn:
• The architecture of a RAG system (LLM + embedding model + vector database).
• How to ingest and index data for efficient retrieval.
• Building a pipeline to connect a chatbot with enterprise or project-specific data.
• A live demo of a working RAG chatbot.
• Best practices: handling hallucinations, scaling queries, and securing private data.

Prerequisites:
• Intermediate Python experience.
• Basic knowledge of machine learning or databases.