Production-Grade RAG with C# and .NET is the complete guide to building Retrieval-Augmented Generation systems on the Microsoft stack — from an 80-line Hello World to a fully deployed, audited, EU AI Act-aware Azure pipeline.
Most RAG material is written for Python. This book is written for you: a .NET developer with a few years of C# experience who wants to ship something real with your company's data. Across seven parts, twenty-five chapters and 668 pages, you'll build Contoso SmartDocs, a running enterprise project that grows chapter by chapter into a production system spanning six document silos — powered by the Microsoft Agent Framework 1.14, Microsoft.Extensions.AI, Azure AI Search, Qdrant, Neo4j, and .NET 10 / C# 14.
You'll learn to:
- Build the core pipeline component by component: embeddings, chunking with Anthropic's Contextual Retrieval, multimodal content, vector databases, indexing strategies, hybrid retrieval with RRF, reranking, and first-class SSE streaming
- Add query intelligence: metadata filters, query construction, routing, and conversational multi-turn rewriting
- Go beyond vectors with graph databases, hybrid storage, GraphRAG, LazyGraphRAG, and Vectorless RAG
- Serve retrieval as a tool over the Model Context Protocol and orchestrate multi-agent RAG workflows with agentic memory
- Ship it for real: a 100-query golden eval gate on every pull request, latency and cost optimization, drift and model migration, prompt-injection defense, grounded citations, EU AI Act audit logging, and a full Bicep-deployed Azure capstone
Every chapter ships runnable code in the companion repository. Every snippet traces to a specific file. Every sample compiles; all 458 tests pass on a clean clone. The architecture is deliberately clean — small, named interfaces with swappable implementations — so what you learn maps directly onto the system you need to build, not just the one in the book.
Nine appendices round it out: worked exercise solutions, a design-pattern flashcard set, a vector database comparison, embedding benchmarks, a Python-to-.NET Rosetta Stone for readers arriving from LangChain or LlamaIndex, a debugging decision tree, prompt patterns, and the math behind RAG.
This is the production-concerns RAG book the .NET ecosystem has been missing. If you can write C#, you can ship RAG.
