As long as the AI has access to their ongoing purchase histories it’s actually quite easy to have this for day to day situations.
Where it would have difficulty is unexpected spikes in grocery usage, such as hosting a non-annual party.
In theory, as long as it was fine tuned on aggregate histories it should be decent at identifying spikes (i.e. this person purchased 10x the normal amount of perishables this week, that typically is an outlier and they’ll be back to 1x next week), but anticipating the spikes ahead of time is pretty much impossible.
Both of these problems could feasibly be solved by user input. If you had the ability to set rules for your personal experience, problems like that would only last as long as it takes the user to manually correct.
Like, “Ai, I bought groceries for a party on March 5th. Don’t use that bill to predict what I need” or “stop recommending butter that isn’t this specific brand”
As long as the AI has access to their ongoing purchase histories it’s actually quite easy to have this for day to day situations.
Where it would have difficulty is unexpected spikes in grocery usage, such as hosting a non-annual party.
In theory, as long as it was fine tuned on aggregate histories it should be decent at identifying spikes (i.e. this person purchased 10x the normal amount of perishables this week, that typically is an outlier and they’ll be back to 1x next week), but anticipating the spikes ahead of time is pretty much impossible.
Both of these problems could feasibly be solved by user input. If you had the ability to set rules for your personal experience, problems like that would only last as long as it takes the user to manually correct.
Like, “Ai, I bought groceries for a party on March 5th. Don’t use that bill to predict what I need” or “stop recommending butter that isn’t this specific brand”