(Replying to PARENT post)
What are you even talking about, why would anyone be building chatbots at this point? Chatbots are like the hello world of using an LLM API, it has nothing to do with what this article is about.
👤tinco🕑2y🔼0🗨️0
(Replying to PARENT post)
For some use cases, legal reasons such as proprietary/private data, copyright, terms of service, prevent the use of a 3rd-party API.
On the other hand, directly using an off-the-shelf model, even the best ones, may not meet your performance requirements.
That’s where fine-tuning an open LLM is necessary.
👤7d7n🕑2y🔼0🗨️0
(Replying to PARENT post)
1) Dropbox can be replicated in a couple of hours with SFTP (or whatever was that iconic HN comment)
2) the devil is in the details. How do you get the data out of documents? Are they pdfs? Do they have tables? Do they have images? Sure, creating embeddings from text is simple and shoving that into a prompt to get an answer is easy, but getting that text out from different documents can be tricky.
Source: finished a chat to your documents project a month ago
👤rolisz🕑2y🔼0🗨️0
(Replying to PARENT post)
Practically speaking the starting point should be things like the APIs such as OpenAI or open source frameworks and software. For example, llama_index https://github.com/jerryjliu/llama_index. You can use something like that or another GitHub repo built with it to create a customized chatbot application in a few minutes or a few days. (It should not take two weeks and $15,000).
It would be good to see something detailed that demonstrates an actual use case for fine tuning. Also, I don't believe that the academic tests are appropriate in that case. If you really were dead set on avoiding a leading edge closed LLM, and doing actual fine-tuning, you would want a person to look at the outputs and judge them in their specific context such as handling customer support requests for that system.