Let's Build It: AI Chatbot with RAG in .NET Using Your Data

Build a Retrieval-Augmented Generation (RAG) chatbot that can answer questions using your data.

Retrieval-Augmented Generation (RAG) is a transformative AI architecture that enables large language models to answer questions using your specific data rather than relying solely on their training knowledge. It combines the power of semantic search through vector embeddings with the natural language capabilities of LLMs, creating AI systems that can provide accurate, contextual, and verifiable responses grounded in your custom knowledge base. RAG has become the cornerstone of modern AI applications, powering everything from intelligent customer support and internal knowledge bases to research assistants and domain-specific Q&A systems. Unlike traditional chatbots or pure LLM solutions, RAG-based systems can cite their sources, stay current with your latest data, and dramatically reduce hallucinations by anchoring responses in retrieved documents. Companies from startups to enterprises are adopting RAG to unlock the value in their documentation, support tickets, and proprietary content.

In this hands-on course, instructor James Charlesworth will take you from understanding vector embeddings and semantic search to building a production-ready RAG chatbot in .NET with OpenAI, Pinecone, and advanced techniques like HYDE for enhanced retrieval accuracy.