@hackproduct9: How AI answers from your docs — RAG (Retrieval Augmented Generation) from scratch, in 3 steps. 📚 Stop asking "why does my chatbot make things up?" Ask: did the right chunk ever reach the model? 1️⃣ Prepare — once, offline Docs, PDFs and web pages → chunking → embeddings → vector DB. → This is your indexed knowledge. An embedding model does this part, not the LLM. 2️⃣ Retrieve — every question "Can I get a refund?" → embed the question → search → top-5 chunks closest by meaning → context. → Better chunks and better search beat a bigger model. 3️⃣ Generate — answer with sources System prompt + user query + retrieved context → LLM → is the answer grounded in the context? ✅ Yes: final answer + citations 🔁 No: rerank, filter, retry 👀 The part nobody says: most "hallucinations" in RAG are retrieval bugs. If the refund policy never made the top 5, the LLM was guessing. Rule: prepare once, retrieve per question, answer with sources. Where teams go wrong: skipping the grounded check and the citations. The answer sounds right, nobody can verify it, and the first wrong refund becomes a ticket. 📸 Screenshot the last frame — the whole RAG pipeline on one screen. Save it before your next RAG build. Follow @hackproduct — we turn scary AI-engineering concepts into things you can actually ship. ⚡ . . #AIengineering #machinelearning #softwareengineering #RAG #LLM

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Friday 16 October 2026 02:47:44 GMT