PostgreSQL for AI: Master SQL, pgvector, RAG & Vector Search

About this course

Master the database engine powering the modern AI revolution.

Every major LLM application requires a reliable, blazing-fast backend to store, filter, and search high-dimensional embeddings. While many developers scramble to adopt complex, standalone vector databases, industry leaders are turning to the most trusted relational database on earth: PostgreSQL.This course is a comprehensive blueprint for building production-grade, AI-native backends using PostgreSQL and pgvector. Whether you are starting from scratch with relational database fundamentals or you are a seasoned performance engineer looking to master advanced vector geometry, this course bridges the gap between traditional relational database management systems (RDBMS) and modern AI infrastructure.

The Curriculum SnapshotSectionCore FocusKey Technologies & Concepts1 & 2: Database FoundationsSQL Mastery & OptimizationB-Trees, GIN, BRIN, CTEs, Window Functions, Optimizer Execution Plans3 & 4: The Vector Revolution

High-Dimensional Searchpgvector, Cosine/L2 Distance, ANN, IVF vs. HNSW Indexing5: Production RAG PipelinesLLM Architecture Patterns

Document Chunking, LangChain, LlamaIndex, Metadata Filtering6: Scaling & Operations

Enterprise Deployment

Partitioning, Row-Level Security, Read Replicas, Cloud Deployment

What You Will Master In This Course:

Advanced pgvector Engineering: Install, calibrate, and optimize the pgvector extension to store and query high-dimensional machine learning embeddings.

Blazing-Fast Similarity Searches: Demystify the math behind Cosine, L2, and Inner Product distances. Implement HNSW (Hierarchical Navigable Small World) and IVF indexes to accelerate vector matching at scale.

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