@entrepreneurship93: Comment RAG and I'll send you the RAG flow cheat sheet, from document chunks to the final answer. How can an AI answer questions about a private document it was never trained on? Picture a 600-page employee handbook. Someone asks how many vacation days can roll into next year, but the policy uses different words and the model has never seen the document. ⚡ Split the handbook into small chunks, so the system can search a few paragraphs instead of processing all 600 pages for every question ⚡ Turn each chunk into an embedding, a list of numbers that places similar meanings near each other even when the wording is different ⚡ Embed the question too, find the nearest chunks in the vector index, and add the best matches to the prompt ⚡ When the policy changes, update and re-embed those pages. The model can use the new text without being retrained The handbook stays outside the model. RAG retrieves the right page and places it in the prompt just before the model answers. Comment RAG for the flow cheat sheet. What document would you connect to a RAG system first? 👇 #rag #artificialintelligence #llm #aiengineering #vectordatabase
