Vector Databases: The Storage Layer for AI Applications
Vector databases store and retrieve high-dimensional embeddings — the numerical representations that power semantic search, recommendation engines, and retrieval-augmented generation. Unlike relational or document databases, they are built to answer questions like "find the 10 most similar items to this query" at scale and with low latency. This track covers the three tools that dominate the space in production: Pinecone, Weaviate, and pgvector — a managed cloud service, a self-hosted open-source engine, and a PostgreSQL extension respectively.
What You Will Learn
You will start with how vector databases work internally, then move to generating and managing embeddings for real datasets. From there you will build with each platform in depth: indexing and querying with Pinecone, modelling data with Weaviate's schema and its GraphQL interface, and adding vector search to an existing Postgres stack with pgvector. The track places particular emphasis on Retrieval-Augmented Generation (RAG) — building pipelines that ground LLM responses in your own data — and then pushes into advanced territory: multi-stage RAG strategies, pgvector index tuning and query optimisation, and Weaviate's hybrid search and module system. The final courses cover deploying and operating vector database applications in production and evaluating emerging hybrid approaches.
The Learning Path
Twelve courses span A1 through C2. The track opens with Fundamentals of Vector Databases and two parallel A2 introductions — one for Pinecone, one for pgvector — before converging at B1 with embedding generation and Weaviate's core concepts. The B2 layer adds Advanced Pinecone Operations and the first RAG course. The C1 block is the longest section: Advanced Weaviate Capabilities, Optimizing pgvector Performance, Advanced RAG Strategies, and Productionizing Vector Database Applications. The track closes at C2 with Future Trends & Hybrid Approaches, which surveys the converging landscape of vector, keyword, and graph retrieval.
How It Works
Each course is split into short, hands-on lessons you complete in the built-in code editor with real-time feedback. An AI tutor is available whenever you get stuck, so you can work through index configuration, embedding pipelines, and query optimisation at your own pace without leaving the app.
