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RAG looks complicated… until you reduce it to 4 steps. 🧠🔎 1️⃣ Index Documents → text → chunks → embeddings → vector database. 2️⃣ Retrieve User asks a question → embed the query → semantic search → fetch the most relevant chunks. 3️⃣ Augment Take those chunks + the original question → build better context for the model. 4️⃣ Generate Send that enriched prompt to the LLM → generate a grounded response. That’s the core loop: Retrieve → Augment → Generate The important mental model: RAG does not magically teach the LLM new knowledge. It gives the model the right context at inference time. That one distinction explains why RAG is so useful for: 📚 internal docs 💬 support assistants 🧑‍💻 codebases 📄 policies 🛒 product catalogs 🏢 enterprise search And yes — production RAG gets deeper with reranking, hybrid search, metadata filters, query rewriting, evals, and caching. But if you understand this picture first, everything else becomes much easier. Save this one. You’ll reuse this mental model a lot. ⚡ #RAG #AIEngineering #LLM #GenerativeAI #VectorDatabase