The LLMs are able to hone in on the details and provide interesting responses like "What questions were asked of the minister that they failed to address" and "What should the opposition leader have mentioned in their response that the minister would have found difficult to answer"
Crucially, they're well transcribed: https://api.oireachtas.ie/
For e.g, if the question is: "What should the opposition leader have mentioned in their response that the minister would have found difficult to answer."
An embedding based search will find it fairly difficult to match this against a text. Based on my experience, you have to figure out what is a nonanswer first (i don't think that's easy but gpt4 is very good at a lot of language based stuff.)
you can try Q1: question A1: Answer then prompt GPT, do you think A1 answers the question and then save it. And then, Q1: question, A1: Answer, Q2: Follow up based on the following questions, do you think A1 answers Q1 and then save it to a db.
You can then augment it in the code with our own knowledge of how politicians lie, using certain words etc :) to improve what gpt4 might miss...
https://chat.openai.com/share/c736abf4-ae3c-4fbd-9427-b7d2f9...
I was also thinking about a society of bots type application where you could have autonomous bot researchers, debaters, judges and audience. Would be interesting to feed in the topics and grab some popcorn
Leaving aside the bots that are trained to answer "as an LLM I do not have opinions..." it's going to be a very basic probabilistic yes or no based on tone and numbers of pros and cons rather than knowledge of the surrounding political context and higher order reasoning about the accuracy of the claimed pros and cons.