Vector databases, knowledge graphs, LLM integration, PostgreSQL for AI, and secure & compliant RAG — structured paths from your first query to production-ready systems.
AI Knowledge Hubis a production AI infrastructure curriculum covering vector databases, knowledge graphs, LLM integration, PostgreSQL for AI, and secure & compliant RAG. Engineers progress from a first vector-database query to capstone production system designs across 74 hands-on lessons in five tracks. Every module ships with code exercises and knowledge checks — built for teams deploying retrieval-augmented generation into regulated industries. The five-lesson Vector Databases beginner course is free.
Authored by DP Regan, MS Computer Science & Cybersecurity, NYU.
Five tracks, beginner to advanced — pick a row, pick your depth. Every level is a self-paced, 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, cross-encoder reranking, 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.
Take the beginner RAG pipeline to production: cross-encoder reranking, agentic RAG with tool use, conversational memory, RAG evaluation frameworks, and a production RAG service capstone.
Operate LLM-powered RAG at scale: observability and tracing, fine-tuning the generator with LoRA/PEFT, inference and serving optimization, advanced retrieval-generation techniques, and a platform-scaling capstone.
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, graph neural networks, 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, the pgvectorscale + pgai stack, row-level security and encryption, production observability and drift, and a platform-design capstone.
Coming soon
A focused five-day track on securing production RAG for regulated industries: threat modeling, prompt-injection and jailbreak defense, PII/PHI de-identification, data-layer security, and provenance, audit, and compliance.
Operate secure RAG at scale: adversarial red-teaming and guardrail evaluation, model and supply-chain provenance, privacy-preserving and confidential retrieval, securing agentic multi-tool systems, and a continuous-compliance and incident-response 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, PostgreSQL for AI, and secure & compliant RAG — at the depth required for engineers shipping retrieval-augmented systems into regulated industries. The curriculum spans beginner through advanced across five tracks, each culminating in hands-on capstones.
The five-lesson Vector Databases beginner course is completely free — no signup required. Full access to all 74 lessons across the five tracks, with hands-on exercises, quizzes, and completion certificates, is $99/month or $490/year on the Pro plan. Cancel anytime.
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 74 lesson-days across five tracks. Vector Databases and Knowledge Graphs span 16 days each, PostgreSQL for AI spans 17, and LLM Integration spans 15 — each across three levels (beginner, intermediate, advanced); Secure & Compliant RAG adds a 10-day, two-level path (intermediate and advanced). Days run roughly 45–75 minutes. Most learners complete a full level in 1–2 weeks at a working-professional pace.
No. Lessons run in the browser — the free Vector Databases starter course requires no install and no signup. Hands-on practice exercises ship with reproducible environments — local Docker for deeper labs, or browser-based notebooks for quick experimentation.