Jun 27, 2023Liked by Aurimas Griciūnas

Largely the accepted way of doing this for now, but it's far from perfect. In my experience your system prompt has to be very exact in order to stop it hallucinating. The other problem is with chunking the data itself - if you're passing several large documents and the chunks are of set length, it can and often will infer bad context just based on chunk cutoffs. Small, semi-structured individual documents seem to perform far better than chunking.

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Jun 25, 2023Liked by Aurimas Griciūnas

Can you an end to end project that covers:

RAG assistance In LLM based chatbots for context and reducing mistakes

Fine tuning open source LLM mode

Mixe media vector embedding

Re ranking things like that..

It will be onebigassproject for sure.

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Hi Aurimas, have there been any updates to the process you described above since last June? Or is this still the widely accepted process/method of building this out? Curious to hear your thoughts.

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Perhaps newb question: for step 9. on the last solution, is the LLM being described a commercial public API? Assuming it doesn't mean an independently trained one. Thanks

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