Unlock the Power of RAG Architectures in One Glance!!!

RAG = Retrieval-Augmented Generation.

It’s a technique where a model retrieves information from external sources (like databases, documents, APIs) before generating a response making answers more accurate, up-to-date, and reliable.

This cheat sheet simplifies everything you need to know:

✅ Naive RAG – Pull chunks → feed to LLM → generate response.
✅ Retrieve-and-Rerank – Pull chunks → rerank best ones → smarter answers.
✅ Multimodal RAG – Text, images, videos... your LLM’s new playground.
✅ Graph RAG – Not just chunks, but structured knowledge graphs for deeper reasoning.
✅ Hybrid RAG – Combine multiple retrieval strategies for max coverage.
✅ Agentic RAG (Router) – AI agent decides where and how to fetch the best info.
✅ Agentic RAG (Multi-Agent) – Teams of specialized agents work together (search engines, web, Gmail, Slack... different tools combined).