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Retrieval (RAG) — Course Notes

These notes follow the Ultimate RAG Bootcamp section-by-section, so each note maps to exactly what's taught in the course. I'm writing them up in my own words as I go, with the key ideas, the code I understand, and the gotchas.

Progress: Sections 1–14 studied — foundations through vector stores, advanced chunking, hybrid search, query enhancement, multi-modal RAG, the updated LangChain v1 essentials, and LangGraph basics. Sections 15–29 are coming soon; I publish each as I finish it.

Course progress~48% · 14 / 29 sections

Course progress tracker

#SectionStatus
1Introduction✅ Done
2Introduction to RAG✅ Done
3Core Components in RAG✅ Done
4VS Code & Anaconda Installation✅ Done
5Data Ingestion & Data Parsing Techniques✅ Done (10/10)
6Vector Embedding & Vector Databases✅ Done (5/5)
7Vector Stores & Vector Databases✅ Done (13/13)
8Advanced Chunking & Preprocessing✅ Done (4/4)
9Hybrid Search Strategies✅ Done (8/8)
10Query Enhancement✅ Done (4/4)
11Multi-Modal RAG✅ Done (2/2)
12Getting Started with AI Agents & Agentic AI⏳ Notes pending
13Updated LangChain Hands-On (v1)✅ Done
14LangGraph Basics✅ Done (16/16)
15Agents Architecture🔲 Soon
16Agentic RAG🔲 Soon
17Autonomous RAG🔲 Soon
18Multi-Agent RAGs🔲 Soon
19Corrective RAG🔲 Soon
20Adaptive RAG🔲 Soon
21RAG with Persistent Memory🔲 Soon
22Cache RAG with LangGraph🔲 Soon
23VectorLess RAG with PageIndex🔲 Soon
24Guardrails🔲 Soon
25LLM Gateways🔲 Soon
26Chatbot & RAG Evaluation🔲 Soon
27Graph Databases & Cypher with LangChain🔲 Soon
28Practical GraphDB with LangChain🔲 Soon
29End-to-End RAG Document Search Project🔲 Soon

What's live now

Next: Section 1 · Introduction →