You could probably ask ChatGPT how to get started with GPT, but I think a lot of people are building apps taking advantage of GPT's Fine Tuning ability. https://platform.openai.com/docs/guides/fine-tuning

If I take advantage of this for allowing my customers to ask questions about our product documentation, how do I limit questions to questions about my product documentation? I don't want to be paying Open OPI for questions unrelated to my product.

This is really a good question. Could be interesting also which is the best method to provide to GPT a Product documentato and ask question inherents to that and nothing else.

One idea would be to use a much cheaper (and faster) classifier to come back with a "yes" or "no" if the question asked is about your product documentation.

Using Ada or Babbage is about 1% of the cost of Davinci (and Curie is 10% the cost of Davinci).

Without any real tuning, this responds quite promptly (and the various tests I've done, correctly):

    curl https://api.openai.com/v1/completions \
      -H "Content-Type: application/json" \
      -H "Authorization: Bearer $OPENAI_API_KEY" \
      -d '{
      "model": "text-ada-001",
      "prompt": "Identify if the following question is about the Olympic Games.  Answer with {yes}, {no}, {maybe}.\n\nWhat category is pole vaulting in?",
      "temperature": 0.7,
      "max_tokens": 38,
      "top_p": 1,
      "frequency_penalty": 0,
      "presence_penalty": 0
    }'

> What category is pole vaulting in?

>> Yes

Well, at least you can't say the documentation is out of date.

I was just after something simple in its training set to demonstrate the simple classification that could be used as a check before passing the request on to a more expensive fine tuned model.

Honestly the best comment I've read on here in a long time. Cheers.

It's way easier and cheaper to use embeddings for these sorts of use cases.

It probably is - I just haven't toyed with that part of the system yet.

I'm going to coin the cyber-security finding "Bill exhaustion via open ML prompts".. wonder how long till it's in the Owasp top X

Openai has documentation and guides in their blog about how to interface with the GPT API. But in this case, you can also look directly at the source code published here.

https://platform.openai.com/docs/introduction

https://github.com/arc53/docsgpt/tree/main/application

If you're looking for a low-code solution, I've written up how we handle this here: https://relevanceai.com/blog/use-all-of-your-blog-posts-and-...

This looks like a good way to explore how we could start using AI at our organization. Say we have a PDF of an instruction manual and we'd like to make a workflow that lets us use Ask Relevance. There doesn't seem to be a way to vectorize the content of a PDF. The option isn't presented in the drop down menu. It seems to only offer CSV-related processing, although I selected a PDF as the data type for upload. Am I using it wrong?

If you want join our discord and i can talk you through it