RAG Systems
Days 36-50 (15 days)
The Most Important Pattern in Production AI
RAG (Retrieval Augmented Generation) is how you make AI know YOUR data. It's the #1 requested feature: "Can I chat with my documents?"
This is the longest phase because RAG is the most employable skill. By the end, you'll build a complete customer support bot that answers questions from your knowledge base.
What Problems Will You Solve?
Daily Schedule
RAG Architecture Overview
What RAG is, hallucination problem, pipeline design
Document Loading & Parsing
PDFs, HTML, text extraction, metadata
Chunking Strategies
Fixed-size, semantic, overlap, tradeoffs
Vector Databases: ChromaDB
Local vector storage, collections, queries
Vector Databases: Pinecone
Cloud vector DB, scaling, multi-tenancy
Building Chat with PDF
Complete RAG pipeline, source citations
Retrieval Strategies
Similarity search, MMR, diversity
Hybrid Search
BM25 + semantic, reciprocal rank fusion
Re-Ranking for Quality
Cross-encoders, Cohere Rerank, two-stage retrieval
Query Enhancement & HyDE
Query rewriting, typo handling, multi-query
RAG Evaluation
Ragas, precision, recall, faithfulness
Conversational RAG
History-aware retrieval, question condensation
Advanced RAG Patterns
Compression, multi-modal, GraphRAG concepts
Project: Customer Support Bot
Full production RAG with knowledge base