RAG
6 guides on RAG.
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.