Curriculum
AI & RAG Part 1: Search & Retrieval
Master search systems, vector embeddings, and retrieval-augmented generation from first principles — the foundation of every modern AI application.
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Search Foundations
What search is, why it is hard, and the two main paradigms — lexical and semantic.
Vector & Embeddings
The math and infrastructure behind semantic search — embeddings, vector stores, and pgvector.
Advanced Retrieval
Combining signals, fusing ranked lists, and reranking to get the most relevant results.
RAG & Query Intelligence
Building retrieval-augmented generation systems and improving queries before they hit the index.
Applied RAG
Practical techniques that separate a prototype RAG system from a production-quality one.
Career Readiness
Translating retrieval knowledge into job-ready fluency for your specific role.