Build RAG applications with Azure Database for PostgreSQL: Review

The "Exercise - Build a RAG application with PostgreSQL and Python" unit is where the real work happens. But the sandbox took almost ten minutes to spin up. I almost closed the tab, but I’m glad I didn’t. Unlike a lot of tutorials that just show you code snippets, this one gives you a full, working Python script and a temporary Azure environment to run it in. You’re not just reading about RAG; you’re actually building a small question-answering app connected to a real Postgres database with the `pgvector` extension. I could copy the entire script, tweak it, and have a personal project skeleton ready to go.

The module does a decent job explaining the basic RAG pattern and why you’d use something like `pgvector` for similarity search inside your database. It feels very practical. The theory is just enough to get you to the code. I followed along, ran the setup, ingested the sample data, and asked it questions. It worked.

My only real complaint is that it breezes past some of the trickier parts. The final sections on improving accuracy and GraphRAG are basically just teasers. They tell you these concepts exist but don't give you another exercise to try them. I get it, it's a short module. But I was left wanting another sandbox for the advanced stuff. Still, for an hour and a half, going from zero to a functioning RAG app on a real cloud service is pretty solid.