AI Memory Should Be Product State, Not a Hidden Prompt Trick
I have been rebuilding how I think about AI memory.
The first version sounded simple enough: summarize the conversation, save the useful parts, and feed them back into the next prompt.
Disclosure: I used AI as an editing assistant while adapting this article. The product details, architecture choices, and opinions are mine.
That summary-first model works for some products. If an assistant needs to remember that a repository uses pnpm, or that a team calls its staging branch preview, a compact preference note may be enough.
But the model breaks down when memory affects a personal, reflective, or long-running product experience.
In that kind of product, the hard question is not:
What can the model remember?
It is:
What should be carried forward?
Who approved it?
When can it enter the next prompt?
When should the system deliberately forget it?
That is why I no longer think of AI memory...
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