Good points 
Thanks for your reply
One could think up an infinite of use-cases connecting LLMs together to achieve a goal, but here’s a simple down-to-Earth example…
Say there is a forum for researchers who need to find new research papers quickly and easily. One could just use the already-available http library in the AI plugin custom tool builder, then make a tool which sends a request to some database of papers (for example arXiv or peer-reviewed academic journal). But this is essentially just a “one-shot” attempt.
A better approach would be to chain together multiple LLM calls to take a step-by-step approach to the problem:
- chat with the user about what their research topic is (loop this a few times to get lots of details)
- summarize the user’s needs according to a predefined template
- turn that summary into a API request and receive a list of potential matches from the database of papers
- analyze the abstract of each paper to ensure its actually relevant
- present that list to the user and explain why each paper is relevant to their research topic.
This isn’t what I am working on (I don’t necessarily want to hand out my business model and architecture ), but it is a simple and realistic example which is actually being addressed by some developers.
Essentially what I am trying to sell users (via the lovely subscriptions plugin) is access an AI bot that is more complex than just a system prompt with a simple tool hooked up to some service which shoots back some data (stocks prices, etc.). More complex in the sense that it solves a problem / addresses a user query in multiple steps, each step utilizing an LLM with a different system prompt. This is essentially what the new OpenAI o1 model does. 