Module 10
RAG & Knowledge Systems
Build AI that deeply knows your data
Lessons
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10.1
RAG Architecture Deep Dive
Locked60 min
10.2
Embeddings & Semantic Search
Locked75 min
10.3
Production Vector Databases
Locked60 min
10.4
Advanced Retrieval Strategies
Locked90 min
10.5
Enterprise Knowledge Systems
Locked80 min
Expert Lens
Founder
Think like a founder: what user pain, distribution channel, pricing, and retention loop does this AI capability unlock?
Systems
Think like a first-principles systems builder: separate demos from durable products, and measure the bottleneck the model actually removes.
Infrastructure
Think like an AI infrastructure leader: account for data, latency, cost, reliability, evaluation, and deployment constraints before scaling.
Objectives
- Build production RAG systems from first principles
- Implement vector databases at scale
- Create enterprise AI knowledge bases
- Deploy semantic search that actually works
Capstone
Enterprise Knowledge AI
Build a production RAG system: 500+ documents, hybrid search, re-ranking, and a polished chat interface.
A deployed knowledge AI you can immediately demo to enterprise clients.
Real Examples
- Create an internal knowledge assistant that cites policy, docs, and support material instead of guessing.
- Compare naive retrieval, reranking, citations, and access-control filters before exposing answers to users.
Mastery Checks
- Build a small retrieval system with source attribution.
- Test hallucination, missing-context, conflicting-source, and stale-document cases.
- Explain chunking, embeddings, ranking, citations, and access control tradeoffs.