Get your bearings and learn what you're gonna learn about.
Look, you've heard "AI" ten million times lately. You're tired. I'm tired too.
But vector search is happening whether you like it or not.
Good news: you don't need to understand the math or train models. You just need to know how SQL Server stores these things and how to query them.
That's what this section does.
This is where it gets useful. No more theory - you're building actual features people will use. "Show me similar questions" without writing a nightmare LIKE clause. "Find related answers" without manually tagging everything. The kind of stuff product managers ask for and you can actually deliver.
Here's the truth: vectors are great until someone searches for "SQL-DMV-1234" and your semantic search returns philosophical discussions about database monitoring. Sometimes you need exact matches. Sometimes you need meaning. Usually you need both. Learn when to use what and how to combine them without creating a Frankenstein query.
Scanning a million vectors works exactly once before someone asks why the query takes 30 seconds. Enter vector indexes - specifically DiskANN. The good news: massive speedup. The bad news: it's still in preview. Learn what works, what doesn't, and what "approximate" actually means for your results. It's probably fine.
Microsoft finally made it so you don't need Python scripts running somewhere. AI_GENERATE_EMBEDDINGS lets you create vectors from T-SQL like a civilized person. Point it at OpenAI, Azure, or your local Ollama instance and suddenly embeddings are just another column. This is the "it just works" part of the course.
Congratulations, you have embeddings. Now someone updates a record and your vectors are stale. Or someone pastes the entire Lord of the Rings into a text field and your model chokes. Welcome to production. Learn to handle updates without regenerating everything, chunk long content without losing meaning, and build triggers that won't make you hate your life.
You know what's fun? Getting paged at 2am because vector search is slow and you have no idea why. You know what's better? Actually understanding execution plans for VECTOR_DISTANCE, knowing what metrics matter, and having monitoring in place. This is the DBA section - the part where you learn to keep vector search running when it counts.
RAG is everywhere now - feeding LLMs relevant context from your data so they don't hallucinate. Turns out SQL Server makes a perfectly good knowledge base for it. Learn to search across multiple tables, format results for LLM consumption, and build the kind of AI features everyone's talking about.
I'm terrible at goodbyes, but this is the last video (at least until Cumulative Updates start coming out).
This video is about where to go and what to do when you finish and actually know what you're doing.