User Profile Engine for Personalized AI Agent Behavior

Added Jun 2025 3 design docs

Most AI assistants treat every user identically: a first-year student and a subject-matter expert get the same tone, the same depth, and the same examples. That one-size-fits-all behavior limits how useful assistants can be in education and technology products, where the right answer depends heavily on who is asking. Product teams need a clean way to capture what they know about a user and have the assistant honor it consistently.

This project has the intern build the profile layer that makes such personalization possible. Using Python and FastAPI, the intern implements endpoints for creating, updating, and retrieving structured user profiles covering preferences such as expertise level, learning goals, preferred response length, and tone. The core of the work is the injection mechanism: a service that translates a stored profile into system-prompt context for OpenAI models so that the same question yields appropriately different answers for different users. The intern also builds comparison views that show side by side how responses change as profile attributes change, making the personalization inspectable and testable.

The finished system demonstrates user-centric AI design, prompt-context engineering, persistent state management behind an API, and a disciplined approach to evaluating whether personalization is actually working.

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