How RAG actually works (clearly explained in under 2 mins):

RAG (Retrieval-Augmented Generation) is a system that retrieves relevant data and feeds it into an LLM before generating a response.

It lets models answer questions using external knowledge, not just what they were trained on.

If you’re building with these patterns, here's a great guide on scaling multi-agent RAG systems: https://codewithdhanian.gumroad.com/l/miwqgy

Here’s a simple mental model to understand it:

𝟭) 𝗗𝗮𝘁𝗮 𝗶𝘀 𝗶𝗻𝗴𝗲𝘀𝘁𝗲𝗱
↳ Documents (PDFs, docs, APIs) are collected and split into chunks
↳ Each chunk is cleaned and formatted ready for embedding

𝟮) 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 𝗮𝗿𝗲 𝗰𝗿𝗲𝗮𝘁𝗲𝗱
↳ Each chunk is converted into a vector representation
↳ Similar meaning → closer vectors

𝟯) 𝗗𝗮𝘁𝗮 𝗶𝘀 𝘀𝘁𝗼𝗿𝗲𝗱
↳ Vectors are stored in a vector database
↳ Enables fast similarity search across large datasets

𝟰) 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗶𝘀 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲𝗱
↳ The user's query is converted into an embedding (vector representation)
↳ The system compares it against stored vectors and retrieves the most relevant chunks

𝟱) 𝗧𝗵𝗲 𝗟𝗟𝗠 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝘀 𝘁𝗵𝗲 𝗮𝗻𝘀𝘄𝗲𝗿
↳ The query + retrieved context are combined into a prompt
↳ The model generates a grounded response

That's the foundation of RAG. There are several types of RAG, each designed for different use cases and levels of complexity.

If you’re curious what this actually looks like in practice (beyond diagrams), this repo is a great place to start: https://codewithdhanian.gumroad.com/l/miwqgy

It has:
↳ E2E implementations of RAG, AI applications, agents, and systems
↳ Resources covering AI agent architecture, reasoning strategies, and memory systems
↳ Hands-on workshops and guided learning

Star it to keep it bookmarked. This repo will keep growing, and you'll want it on hand as you build.

What else would you add?

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