RAG — Retrieval Augmented Generation
Full RAG architecture, multi-document RAG, RAG vs fine-tuning decision
RAG Architecture — End to End
intermediate~28minDocuments→Chunking→Embeddings→VectorDB→Query→Retrieval→LLM→Output
Why RAG: Custom Data + LLM Brain
intermediate~28minPre-trained LLM knows nothing about your data, RAG bridges the gap
Chunking Strategies for Better RAG
advanced~35minFixed-size vs semantic chunking, chunk_size/overlap tuning, context preservation
RAG vs Fine-Tuning — Decision Framework
intermediate~28minWhen to use RAG, when fine-tuning is better, the real decision tree
RAG Properties: Secure, Reliable, Scalable, Traceable
intermediate~28minWhy RAG is preferred for most enterprise cases — detailed breakdown
Multi-Document RAG
advanced~35minMultiple PDFs, namespace separation, source attribution, scaling
RAG with Different LLMs: OpenAI, Grok, Llama, Ollama
advanced~35minSwapping LLM backends in LangChain, adapter pattern, performance differences
RAG Practical: PDF Research Paper
advanced~35minEnd-to-end with Grok-style API: load PDF → chunk → FAISS → query → answer
Insurance Claims Processing — Real RAG Project
advanced~35minBupa handbook + OCR + RAG → automated claim approval/rejection logic
RAG Failure Modes & How to Fix Them
advanced~35minHallucinations, wrong chunks retrieved, context window overflow, fixes