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Fine-tuning teaches a skill; retrieval supplies the facts. RAG doesn't change the model's brain, it changes what's on the model's desk: before the model answers, the most relevant documents are fetched from a library and pasted into the prompt, so it reads the answer instead of guessing it.
We build it up step by step: why a frozen model hallucinates on data it never saw, the fork between retraining the weights and looking the answer up, how documents become vectors in a map of meaning, how a question finds its nearest neighbours, and how those retrieved chunks get stuffed into the prompt so the model can read, answer, and even cite its source.
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*Contents*
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00:00 - A Mind, Frozen in Time
00:18 - Two Ways to Fix It: Fine-Tune vs Retrieve
00:59 - The Library Is a Map of Meaning
01:25 - Finding the Right Pages
01:47 - Read, Then Answer
02:17 - The Brain and the Bookshelf
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