All Courses
50 hands-on lessons across 4 tracks — vector databases, knowledge graphs, LLM integration, and PostgreSQL for AI — from first principles through production capstones.
Vector Databases
Production vector database engineering: APIs, embeddings, indexing, hybrid search, sharding, and evaluation.
Foundational five-day path through vector databases, embeddings, knowledge graph basics, LLMs, and a first RAG pipeline.
Production indexing, chunking, hybrid retrieval, multi-modal embeddings, and the evaluation discipline that keeps retrieval honest.
Distributed vector search, embedding fine-tuning, multi-vector retrieval, production evaluation, and a capstone production design.
LLM Integration
Connect vector databases and graphs with large language models for production RAG systems.
Knowledge Graphs
Model, query, and reason over knowledge graphs — from property-graph basics and Cypher to entity extraction, hybrid graph+vector retrieval, and production GraphRAG.
Foundational five-day path through property graphs, entity/relationship modeling, Cypher, graph algorithms, and a first GraphRAG system.
Build graphs from unstructured text and run them in production: entity extraction, ontology evolution, hybrid graph+vector retrieval, entity resolution, and graph-retrieval evaluation.
Operate knowledge graphs at scale: graph partitioning and sharding, temporal/versioned graphs, graph embeddings and inference, production GraphRAG, and a platform-design capstone.
PostgreSQL for AI
Use PostgreSQL as a production AI datastore: pgvector, similarity search and indexing, hybrid search, RAG schema design, and scaling embeddings with replication, Citus, and quantization.
Foundational five-day path: Postgres as an AI datastore, installing pgvector, similarity search in SQL, vector indexing, and a first Postgres-backed RAG system.
Production RAG on Postgres: hybrid full-text + vector search, metadata filtering and query planning, RAG schema design, scaling embeddings with partitioning and quantization, and retrieval tuning.
Operate Postgres vector workloads at scale: replication and read scaling, sharding with Citus, incremental embedding pipelines, production observability and drift, and a platform-design capstone.