Memory and recall is an important concept and design pattern that enables the AI product to store and reuse information from past interactions such as user preferences, feedback, goals or task history to improve continuity and context awareness.
Enhances personalization by remembering past choices or preferences.
Reduces user burden by avoiding repeated input requests especially in multi-step or long-form tasks.
Supports complex tasks like longitudinal workflows like in project planning, learning journeys by referencing or building on past progress.
Memory used to access information can be ephemeral (short-term within a session) or persistent (long-term across sessions) and may include conversational context, behavioural signals, or explicit inputs.
How to use this pattern
Define the user context and choose memory type
Choose memory type like ephemeral or persistent or both based upon use case. A shopping assistant might track interactions in real time without needing to persist data for future sessions whereas personal assistants need long-term memory for personalization.
Use memory intelligently in user interactions
Build base prompts for LLM to recall and communicate information contextually (E.g., “Last time you preferred a lighter tone. Should I continue with that?”).
Communicate transparency and provide controls
Clearly communicate what’s being saved and let users view, edit or delete stored memory. Make “delete memories” an accessible action. E.g. ChatGPT offers extensive controls across it’s platform to view, update, or delete memories anytime.