You’re talking about informatics Olympiad and O-1. As for Google’s DeepMind network and math Olympiad it didn’t do 10000 submissions. It did however generated bunch of different solutions but it was all automatic (and consistent). We’re getting there.
Can you share an example of a use case you have in mind of this "explainer + RAG" combo you just described?
I think that RAG and RAG-based tooling around LLMs is gonna be the clear way forward for most companies with a properly constructed knowledge base but I wonder what you mean by "explainer"?.
Are you talking about asking an LLM something like "in which way did the teams working on project X deal with Y problem?" and then having it breaking it down for you? Or is there something more to it?
I'm not the OP but I got some fun ones that I think are what you are asking? I would also love to hear others interesting ideas/findings.
1. I got this medical provider that has a webapp that downloads graphql data(basically json) to the frontend and shows some of the data to the template as a result while hiding the rest. Furthermore, I see that they hide even more info after I pay the bill. I download all the data, combine it with other historical data that I have downloaded and dumped it into the LLM. It spits out interesting insights about my health history, ways in which I have been unusually charged by my insurance, and the speed at which the company operates based on all the historical data showing time between appointment and the bill adjusted for the time of year. It then formats everything into an open format that is easy for me to self host. (HTML + JS tables). Its a tiny way to wrestle back control from the company until they wise up.
2. Companies are increasingly allowing customers to receive a "backup" of all the data they have on them(Thanks EU and California). For example Burger King/Wendys allow this. What do they give you when you request data? A zip file filled with just a bunch of crud from their internal system. No worries: Dump it into the LLM and it tells you everything that the company knows about you in an easy to understand format (Bullet points in this case). You know when the company managed to track you, how much they "remember", how much money they got out of you, your behaviors, etc.
One of the big challenges with clinical trials is making this information more accessible to both patients (for informed consent) and the trial site staff (to avoid making mistakes, helping answer patient questions, even asking the right questions when negotiating the contract with a sponsor).
The gist of it here is exactly like you said: RAG to pull back the relevant chunks of a complex document like this and then LLM to explain and summarize the information in those chunks that makes it easier to digest. That response can be tuned to the level of the reader by adding simple phrases like "explain it to me at a high school level".
