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145 sats \ 4 replies \ @freetx 14h
Technically its "Retrieval Augmented Generation" but what it means practically is when you load documents or data into your LLM so you can reference it in chats.
I've been building my own self-hosted "personal RAG". The goal is to load it with my personal data to help with tax / financial / personal planning, etc
I dare not do that with any publicly hosted AI service, so am building my own.
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100 sats \ 2 replies \ @Signal312 14h
Very interesting, thanks.
Is a potential use case for RAG to set up something like a debater bot - a resource that would be primed to answer arguments on a particular topic.
Maybe I could load up a set of my preferred books on a topic (like bitcoin, or climate change), and then have my own private LLM that would also primarily be about giving good replies for debate? If so, how might one do this?
So, for instance, when someone comes up with a reason why bitcoin won't work, I could come back with some well-structured arguments on that topic.
This is as opposed to thinking, "Darn, I know I've heard a great response to that argument, but I can't remember it now."
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That’s a cool idea! You could definitely create a private LLM or debater bot using RAG with your own book/library content—it’d be like having a personal debate assistant ready with structured arguments anytime. Thanks for sharing.
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35 sats \ 0 replies \ @optimism 12h
Maybe I could load up a set of my preferred books on a topic (like bitcoin, or climate change), and then have my own private LLM that would also primarily be about giving good replies for debate? If so, how might one do this?
You can do this with fine-tuning, see this short primer from #1076304 and for example HF's docs for a more technical guide.
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21 sats \ 0 replies \ @optimism 14h
I too self-host all my production applications that use LLM and all of it is RAG (in the most basic form where I just feed documents to a single-shot summarizer / categorizer / NLP chain.)
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