Learn Vector Databases, Knowledge Graphs, LLM integration, and PostgreSQL for AI with structured learning paths from beginner to advanced. Build production-ready AI applications.
AI Knowledge Hub is a production AI infrastructure curriculum covering vector databases, knowledge graphs, LLM integration, and PostgreSQL for AI. Engineers progress from a first vector-database query to capstone production system designs across 50 hands-on lessons in four tracks. Every module ships with code exercises, knowledge checks, and discussion — built for teams deploying retrieval-augmented generation into regulated industries.
Authored by DP Regan, MS Computer Science & Cybersecurity, NYU.
Four tracks, each structured from fundamentals to production-ready expertise. Every level is five self-paced days with hands-on code, knowledge checks, and a capstone.
APIs, embeddings, indexing, hybrid search, sharding, and evaluation — to a production system design.
Property graphs and Cypher through entity extraction, hybrid graph+vector retrieval, and production GraphRAG.
Connect vector stores and graphs to LLMs: RAG anatomy, prompting, retrieval chains, and a Q&A bot capstone.
Four tracks, three levels each — pick a row, pick your depth. Every level is a five-day, hands-on path ending in a production capstone.
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.
Build a production RAG pipeline from anatomy through deployment, including prompting, chains, vector store integration, and a Q&A bot capstone.
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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.
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.
Founder & Lead Instructor
Every module is grounded in retrieval-augmented systems shipped into regulated industries — defense-industrial-base cybersecurity, clinical documentation, and legal evidence handling — where a RAG pipeline has to be provably correct, not merely impressive.
AI Knowledge Hub teaches the engineering of production AI infrastructure — vector databases, knowledge graphs, LLM integration, and PostgreSQL for AI — at the depth required for engineers shipping retrieval-augmented systems into regulated industries. The curriculum spans beginner through advanced across four tracks, each culminating in a hands-on capstone.
DP Regan, MS Computer Science & Cybersecurity, NYU. AI infrastructure engineer and instructor. Founder of Raptor Cybersecurity LLC. Author of two patent-pending provenance systems for LLM output validation (RAPTOR-CR-2026-001, RAPTOR-CR-2026-002).
A complete production-grade Retrieval-Augmented Generation pipeline: ingestion with layout-aware chunking, hybrid dense + sparse retrieval, prompt and chain composition, citation-aware generation with refusal, gold-set evaluation, and the shadow/A-B rollout discipline that distinguishes a RAG demo from a RAG product.
Yes. The Beginner track assumes general software engineering literacy but no prior machine learning experience. It moves from 'what is a vector database' through a complete first RAG system in five days. Intermediate and Advanced tracks then go deep on indexing, hybrid search, distributed retrieval, embedding fine-tuning, and production evaluation.
There are 50 lesson-days across four tracks. Vector Databases, Knowledge Graphs, and PostgreSQL for AI each span 15 days over three levels (beginner, intermediate, advanced); LLM Integration adds a five-day beginner track. Days run roughly 45–75 minutes. Most learners complete a full level in 1–2 weeks at a working-professional pace.
No. The first day of every track runs in the browser. Hands-on practice exercises ship with reproducible environments — local Docker for deeper labs, or browser-based notebooks for quick experimentation.