RAG vs Fine-Tuning vs Prompt - AI動画分析

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Oh, that's a great way to start, comparing it to Googling yourself. It really sets the stage for how LLMs' knowledge is dependent on their training data and cutoff dates. It makes sense that asking different models about the same thing would yield varying results.
Interesting! So the first way to improve an LLM's answer is by letting it search for new or updated information outside its training data. That makes a lot of sense, kind of like giving it access to a live internet connection. It's cool that they're calling this RAG.
So we've got RAG, then fine-tuning with specialized models, and prompt engineering by crafting better queries. It's like having three different toolboxes to refine the AI's output. I can already see how each of these would have its own pros and cons.

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The video begins by introducing three primary methods for improving responses from large language models (LLMs), starting with Retrieval Augmented Generation (RAG) [0:30]. RAG involves an LLM performing searches for new or updated data that may not have been in its original training set [0:30-1:00]. This retrieved information is then integrated into the model's response. The process involves converting both the user's query and the relevant documents into vector embeddings, which are numerical representations capturing meaning [2:30-3:00]. This semantic similarity allows RAG to find documents that are conceptually related, even if they don't share keywords, enabling more factual and up-to-date answers [3:00-3:30]. However, RAG incurs costs related to performance, processing, and...
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The video begins by introducing three primary methods for improving responses from large language models (LLMs), starting with Retrieval Augmented Generation (RAG) [0:30]. RAG involves an LLM performing searches for new or updated data that may not have been in its original training set [0:30-1:00]. This retrieved information is then integrated into the model's response. The process involves converting both the user's query and the relevant documents into vector embeddings, which are numerical representations capturing meaning [2:30-3:00]. This semantic similarity allows RAG to find documents that are conceptually related, even if they don't share keywords, enabling more factual and up-to-date answers [3:00-3:30]. However, RAG incurs costs related to performance, processing, and...
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