SQL Server Meets AI: Understanding Vector Search in SQL Server 2025

AI applications need somewhere to put data that doesn't fit neatly into rows and columns, high-dimensional vectors representing meaning, not just values. SQL Server 2025 brings that capability directly into the database most teams already run, instead of forcing a separate vector store into the architecture.

Mohamed Tharif B walks through what vectors and embeddings actually are before getting into vector search itself, since the concept trips people up more than the implementation does. Retrieval-Augmented Generation, RAG, gets covered too, along with how vector search fundamentally differs from the keyword and full-text search most DBAs already know well.

A live demonstration in SQL Server 2025 shows what this looks like in practice, not just in theory.

• What vectors and embeddings represent, in terms a DBA can actually use
• How vector search differs from traditional search methods
• Where RAG fits into an application built on SQL Server 2025
• What running vector search inside SQL Server actually looks like, hands-on

Storing AI-ready data next to everything else a database already manages changes what "database administration" means for teams adopting AI features.

• What vectors and embeddings are, and why AI applications depend on them
• How vector search works and how it differs from traditional search
• How RAG (Retrieval-Augmented Generation) uses vector search in practice
• How to work with vector capabilities directly in SQL Server 2025, through a live demo

Vector search in SQL Server 2025 brings AI-ready data management into a platform DBAs already know, rather than requiring a separate system just to support AI applications.