Retrieval-Augmented Generation (RAG) enables AI systems to answer questions using external documents by converting queries into mathematical vectors through embeddings, where similar meanings are stored close together in vector space; the system then uses cosine similarity to find the closest matching information chunks and injects them directly into the AI's prompt, allowing the AI to provide accurate responses based on retrieved information rather than guessing.
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The Secret Behind AI Search: How RAG works🤯 #RAG #AI #MachineLearning #SoftwareEngineering #chatgptAjouté :
Here's the crazy [music] part. AI doesn't actually search words. It searches using math. When you ask a question, [music] the query gets converted into a mathematical vector using embeddings. Similar meanings get stored close together inside vector space. So, JWT ends up near authentication concepts even if the exact words are different. The system then compares vectors mathematically, usually using something called cosine similarity. Smaller angles mean more related meaning. The closest matching chunks get retrieved and injected directly into the AI's prompt. That's how RAG gives AI external knowledge. So, instead of guessing, the AI answers using retrieved information, and that's why RAG is everywhere in modern AI.
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