How much you trust a suggestion is partially due to what context the system is drawing on.
Is it drawing on relevant facts about you?
The quality of the context is a big determinant in how good the results are.
This is one of the reasons the ChatGPT dossier memory feels off to me.
You can't inspect the context to say, "include this, not that".
You can imagine a UX where there's a coactive context drawer at the top of the interaction.
Things in the interaction and that context drawer are all that is given to the LLM, nothing else.
When the drawer is collapsed it shows a short summary.
When you expand it you see trees of context that you've pulled in explicitly.
You can add in trees of context on demand easily.
E.g. "Include information about my nuclear family."
The tree pulls in all of the sub-items that hang off of it.
You can choose to pull in something high up in the tree or low down.
You can delete any tree of context that's not relevant.
There's also a list of auto-included context that the system guesses is useful.
Those are included by default, but can be explicitly added to the included items, just like if you'd added it yourself, or deleted.
The ranking function is: how well does the system predict which trees of context to include, (predicting whether the user would accept or delete a suggestion?)
The user choosing to include or excluding bits from the suggested context is an extremely powerful ranking function.
The user wouldn't even realize that they're training the system for themselves and others by gardening their context.
Only a small number of users would need to do it to help tune it for a whole population.