How much should an AI fitness product remember about the user?

I’ve been thinking a lot about the difference between personalization and real adaptation in fitness software.

A profile with age, goal and experience level is useful, but it still treats each workout request almost like a new session.

A more interesting model is longitudinal: remember what the person actually completed, which movements and muscle groups were trained recently, how difficult the exercises felt, what restrictions exist, and how nutrition has looked over the previous days.

Then the next recommendation is based on that history instead of starting from zero.

The hard part is finding the right balance. Too little memory makes the product repetitive and generic. Too much poorly structured data can create noise and false precision.

For people building AI or health/fitness products: which user signals have actually made personalization meaningfully better in your experience, and which turned out to be unnecessary?