📄️ 4.1 Why RAG?
The hallucination problem, knowledge cutoff, and grounding LLMs in facts.
📄️ 4.2 Loading & Chunking
Chunk size, overlap, and splitting strategies for structured data in RAG pipelines.
📄️ 4.3 Embeddings & Vectors
OpenAI/Cohere embeddings, ChromaDB, FAISS, and similarity search.
📄️ 4.4 Pipeline Architecture
Query → retrieve → augment → generate flow for building RAG systems.
📄️ 4.5 Advanced RAG
Hybrid search, re-ranking, multi-query retrieval, and metadata filtering.
📄️ 4.6 RAG for Structured Data
SQL generation, pandas integration, and cricket scorecards as structured RAG input.