RAG (Retrieval-Augmented Generation) is a technique that solves AI hallucination by enabling AI models to search external information before generating answers. The process involves chunking large documents into smaller searchable pieces with overlap to preserve context, converting text meaning into mathematical vectors through embeddings, and storing these vectors in vector databases. This allows AI to perform semantic search by meaning rather than just keywords, ensuring responses are grounded in actual retrieved information rather than fabricated knowledge.
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RAG Explained in 40 seconds pt: 1#AI #RAG #Programming #MachineLearning #TechShorts #semanticsearch追加:
Modern AI has a massive problem. Ask it about something recent and it might completely hallucinate the answer. And no, fine-tuning doesn't solve this because fine-tuning changes behavior, not knowledge. To fix this, engineers use something called RAG, retrieval augmented generation. It allows AI to search information before generating answers. Massive documents get split into smaller searchable chunks, sometimes with overlap, so important context isn't lost. Then embeddings convert meaning into mathematical vectors and those vectors get stored inside of vector database. So AI can search by meaning, not just keywords. In part two, we'll see how retrieval actually works.
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