#vectordatabase
RAG, semantic search & vector databases — finally explained simple RAG. Semantic search. Vector databases. You've heard these words a hundred times and nodded like you got them. Truth is, they're not four things to memorize — they're pieces of one machine. Here's the whole thing, simple: The problem → an AI only knows what it was trained on and what you paste in. It doesn't know YOUR business. So you have to hand it the right piece of your data — exactly when the question comes in. Semantic search → how it finds that piece. By meaning, not exact words. Ask about your "refund policy" and it pulls the "money-back guarantee" doc, even though the word refund isn't in it. Embeddings → how a computer understands meaning at all. You turn text into numbers, and things that mean similar things get similar numbers. Now "what does this mean?" becomes "how close are these numbers?" Vector database → where all those numbers live, built to find the closest ones fast. RAG → the whole loop. Retrieve the right pieces, add them to the prompt, generate the answer from your real data instead of guessing. And the picture that makes it stick: a librarian who hands the expert the exact three pages by meaning. Same idea, three names. #rag #vectordatabase #aiagents #professorglitch
Professor Glitch11,600 views • 3 months ago
Rag system mistake #RAG #aiautomation #n8n #vectordatabase #professorglitch
Professor Glitch21,900 views • 1 year ago
If you built a RAG and just uploaded a document and left it like that - it's not working as well as it could. Most people miss three things that completely change how accurate your AI answers are. First - metadata. You need to tag your documents. What type is it? What topic? When was it created? Then when someone asks a question your system extracts what they're looking for and matches it to the right tags. No more searching blindly through everything. Second - rephrase the question. Don't just send the exact question to your knowledge base. Build a system that takes that question and rephrases it three different ways. Now you're searching from multiple angles. You pull back more context. Rank the results. Pick the best ones. Way more accurate. Third - chunking. How you split your documents matters. Too big and you feed the AI noise. Too small and you lose context. Break your data with intention. Keep related information together. Metadata. Rephrasing. Chunking. That's the difference between a RAG that kinda works and one that actually performs. #Rag #vectordatabase #aiagent #n8n
Professor Glitch10,900 views • 10 months ago
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