Ground It in Your Actual Data

This is what Microsoft calls retrieval-augmented generation, or RAG — a pattern that extends what a language model can do by grounding its responses in your own content instead of whatever it happened to learn during training. Practically, that means indexing your data first (Azure AI Search handles this over more than ten Azure data source types, including Blob Storage, Data Lake Storage, and SQL, with built-in chunking and vectorization), then, at query time, retrieving only the handful of passages actually relevant to the question and handing those — along with the question — to the model to answer from.

The reason this matters isn't academic. A model answering from general training data will produce something fluent and wrong with equal confidence; a model answering from a retrieval step grounded in your own records can only speak to what's actually in them, and can point to where an answer came from. Microsoft's newer agentic retrieval approach pushes this further — the model itself breaks a vague or conversational question into several targeted searches, runs them in parallel, and returns a structured, cited answer instead of a single best-guess query. (Their older "Azure OpenAI On Your Data" feature, worth flagging if you've read about it elsewhere, is being retired in October 2026 in favor of this newer, agent-based grounding pattern — one more reason we don't hand-roll grounding as a one-off integration.)

How EWO 360° grounds the model

We index a client's Data Lake or database layer with Azure AI Search rather than sending questions to a model with no context, so every answer is retrieved from records that actually exist before the model ever generates a sentence. That's what keeps the output specific to a client's business — inventory levels, case status, a customer's own history — instead of a generic, well-written answer that happens to be about the wrong company.

Next — Step 03: Wire the Action Layer →