Every team wants to put an LLM on top of their own data — but they hit two walls:
- The model doesn't know your data. GPT-4o has never seen your handbook, your support tickets, or your product catalog. Ask it a company-specific question and you get a generic answer or a confident hallucination.
- You can't send confidential data to a public API. Data residency, customer contracts, and trade secrets make that a non-starter.
RAG (Retrieval-Augmented Generation) solves both. You retrieve the most relevant chunks from your data, put them in the prompt as context, and the model answers using only that — with citations, and without your data ever leaving your Azure tenant.
The flow (Azure OpenAI + Azure SQL)
Ingestion (once): chunk your docs, embed each chunk with Azure OpenAI, and store the vectors in Azure SQL's native VECTOR column.
Query (every question): embed the question with the same model, run a VECTOR_DISTANCE search in Azure SQL for the top chunks, build a grounded prompt with citations, then let Azure OpenAI (GPT-4o) answer from that context only.
Two rules that make or break it: the embedding model must match between ingestion and query, and you need a distance threshold so the system says "I don't know" instead of hallucinating on weak context.
Watch the 2-minute explainer
Go deeper
The full production guide — the Azure SQL schema, the ingestion and query services in C#, Managed Identity, cost, latency, and the 8 pitfalls that ruin RAG in production:
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