Welcome
What will you learn in this course?
Who is the course for and prerequisites
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.
About This Course
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.
Course Curriculum
4h 25m 6 sections
What is a Vector?
Semantic Search vs Keyword Search
Creating Embeddings With A Large Language Model (LLM)
Searching With Vectors (Cosine Similarity & Euclidean Distance)
Introduction To Pinecone
Vector Search Architecture in .NET
Creating Embeddings From Wikipedia
Pinecone Index Design & Upsert
Adding a Key Value Store
Building A Search Endpoint
Sentence Splitting and Chunking
Retrieval Augmented Generation (RAG) Explained
Writing LLM Prompts For RAG
Implementing A Generation Step In C#
Answering Questions About Your Data
Creating An Index Of YouTube Transcripts
What Are Hypothetical Document Embeddings
Generating A HYDE Query
Augmented Retrieval And Fusion Step (RRF/MMR)
Building A Basic Chatbot in C#
Tool Calling With LLMs
Adding A Database Search Tool
Multi Search Operations
Using Your Own Data
Meet Your Instructor
James Charlesworth
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