RAG
8 guides on RAG.
Retrieval-augmented generation treated as an engineering problem rather than a diagram: how you split documents, what vector similarity does and does not capture, and how to tell a retrieval failure apart from a generation one before you start tuning prompts.
Vector Search in SQL Server 2025 with EF Core 10: One Less Database
SQL Server 2025 has a native vector type and EF Core 10 speaks it. For a lot of .NET teams that removes a whole moving part from the architecture — here is what it does well, and the ceiling you should check first.
Agentic RAG in .NET: When One Retrieval Is Not Enough
Classic RAG runs one search and hopes. Agentic RAG lets the model decide what to look up, how often, and when it has enough — here is what that costs and how to build it in C#.
RAG vs Fine-Tuning: How to Actually Decide (A .NET Engineer's Guide)
RAG or fine-tuning? A decision framework based on what your problem actually is — knowledge, behaviour, format or cost — plus what each one really costs to run on .NET.
Choosing a Vector Database for .NET: pgvector vs Qdrant vs Azure AI Search vs SQL Server
A practical comparison of the vector stores .NET can talk to — pgvector, Qdrant, Azure AI Search, SQL Server 2025, Redis, Cosmos DB and SQLite — and why the choice matters less than you think.
Foundry IQ and Agent Memory: Managed RAG and Memory for .NET Agents
Foundry IQ gives .NET agents serverless retrieval, knowledge bases, and managed memory. What it replaces in your RAG stack, and when to keep pgvector.
Build a "Chat with your PDF" App in .NET
Let users ask questions about a PDF and get grounded answers. Build a chat-with-your-documents app in C# using extraction, embeddings, and retrieval-augmented generation.
Chunking Strategies for RAG (with .NET Examples)
Bad chunking is the #1 reason RAG underperforms. A practical guide to chunking strategies — size, overlap, and structure-aware splitting — for .NET RAG apps.
Giving Your .NET Agent a Knowledge Base: RAG in C#
Your agent does not know your documents — until you add retrieval. A practical guide to RAG in .NET: embeddings, a vector store, and grounding an agent in your own data.