> First, from a technical standpoint the required context window would be massive if you're looking at a person's career/life holistically. Probably solvable, but definitely something to be aware of.
Why would it be massive? The application layer typically compacts a profile of information about the users financial situation when offered. I doubt many of us have financial situations that would exceed the context window.
> Third, you would need a training dataset of all those input variables and their outcomes to be able to provide any sort of useful output. The first set of people to share everything wouldn't be able to derive any value from the tool, and I think you'd be hard pressed to convince enough people to do it to get a useful dataset.
Would you 'need' a training dataset of input variables and their outcomes for an LLM? Certainly for traditional ML, but the LLM toolcalling can simulate what an astute user should statistically do in their situation based on information on the internet and reason about the different constraints.
Why would it be massive? The application layer typically compacts a profile of information about the users financial situation when offered. I doubt many of us have financial situations that would exceed the context window.
> Third, you would need a training dataset of all those input variables and their outcomes to be able to provide any sort of useful output. The first set of people to share everything wouldn't be able to derive any value from the tool, and I think you'd be hard pressed to convince enough people to do it to get a useful dataset.
Would you 'need' a training dataset of input variables and their outcomes for an LLM? Certainly for traditional ML, but the LLM toolcalling can simulate what an astute user should statistically do in their situation based on information on the internet and reason about the different constraints.