An AI model's identity layer defines how it interacts with a user. This includes how the AI model tracks user progress and encourages continued use. The AI model's identity layer should match the user's goals.
By Pawan Nayar · from the LLOS AI Learning record
An AI model's identity layer defines how it interacts with a user.
This includes how the AI model tracks user progress and encourages continued use. The AI model's identity layer should match the user's goals.
The AI model should track user progress using growth metrics. This means counting questions answered and concepts explored. This method works well for new users. It also suits users focused on general learning. For users with specific goals, like test preparation, a streak metric can be useful. A streak counts consecutive days of use. The AI model can offer both options. The user can choose the tracking method that best fits their needs. When a user has no streak, the AI model can offer a gentle message. For example, 'No streak yet — today can be day 1.' This encourages the user to start. The tone should be positive and inviting. If a user returns after a long break, the AI model can offer a welcome back message. It can also suggest a way to restart. For example, 'Welcome back. Try 10 new questions.' This helps the user re-engage.
A student uses an AI model for general learning. The AI model tracks the number of topics the student explores. This is a growth metric. Another student uses the AI model for exam preparation. The AI model tracks how many days in a row the student studies. This is a streak metric. The AI model adapts its tracking to each student's goal.
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An AI model's identity layer defines how it interacts with a user. This includes how the AI model tracks user progress and encourages continued use. The AI model's identity layer should match the user's goals.