A .NET and Azure field manual for building RAG flows
A RAG field manual maps the full pipeline from ingestion, chunking, , setup, retrieval, augmentation, generation, agents, and evaluation. It is aimed at people working in the .NET and Azure world, where many existing guides feel too focused on Python and LangChain. Each step includes a free or alternative, instead of only pointing to common defaults such as Pinecone or OpenAI.
The material came from studying for Microsoft’s AI-103 certification and is built for visual, structured learning. It can help non-Python understand how the parts of a RAG-based agent system connect.
Key points
- The guide covers the from data intake to evaluation.
- It is written for .NET and Azure users, not only Python users.
- It lists free or choices at each stage.
- It may help builders understand how RAG connects to agent design.
- Cost savings depend on the tools chosen and how they are operated.