Building Natural Language Querying into SQL Server Applications

Increasingly, end users expect to interact with business applications through natural language.
Developers building on SQL Server and Azure SQL databases face the challenge of translating high‑level, conversational questions into accurate, context‑aware queries over operational data.
User queries may need precise answers or relevance‑ranked results using vector search.

This session is aimed at developers and architects building operational applications on SQL Server or Azure SQL who want to safely introduce natural language querying.

Using a retail scenario, we demonstrate how to improve the accuracy and relevance of dynamically generated queries through:

• Multi-agent orchestration to effectively interpret natural language queries and translate these into the correct T-SQL queries or vector searches

• Providing context through dynamically extracted metadata, such as table schemas, foreign keys, and constraints

• Providing business context, such as definitions of seasonal campaigns and other domain logic, using reference documents or captured in the database, to enable agents to reason like a knowledgeable subject matter expert

• Using example queries to help agents build more accurate queries


We will explore examples of application‑driven use cases where operational users need quick answers without leaving the application workflow, for example, “Is a product is in stock?” and “What are the best alternatives to offer?”.

Attendees will leave with patterns to reliably generate T‑SQL and vector queries, enabling them to incorporate natural‑language querying in applications built on SQL Server and Azure SQL databases with confidence.

• Design reliable natural language querying for operational systems by applying multi agent orchestration patterns that translate conversational questions into accurate T SQL and vector search queries on SQL Server and Azure SQL. • Improve query accuracy and safety by grounding AI generated queries with dynamically extracted database metadata (schemas, relationships, constraints) and embedded business context such as domain rules and reference documents. • Apply proven implementation patterns to real application scenarios, enabling users to ask questions like “Is this product in stock?” or “What are the best alternatives?” directly within application workflows—without compromising correctness or control.