Memory makes AI products feel personal. It can also make them feel invasive.
The design question is not how much context you can store. It is which information improves future interactions enough to justify remembering it.
Separate Types of Memory
Conversation state helps complete the current task. User preferences persist across sessions. Product history records actions and outcomes. Retrieved knowledge belongs to the application, not the person.
These categories need different retention, visibility, and deletion rules. Mixing them into one endless transcript makes relevance and privacy worse.
Make Memory Legible
Users should be able to understand why the system knows something. Show saved preferences, provide edit and delete controls, and distinguish explicit facts from model-generated summaries.
Never let a summary silently become a permanent truth. It may contain an inference the user did not make.
Retrieve, Do Not Dump
Passing every remembered detail into every prompt increases cost and creates strange behavior. Retrieve only context relevant to the active task, and keep sensitive information out unless it is necessary.
Memory also needs expiration. Preferences can change, and old project context can become misleading. Track source and timestamp so the product can ask for confirmation when information may be stale.
The right memory feels like continuity, not surveillance. Store less, explain it better, and give the user control over what follows them into the next conversation.