AI-Native Product Design

Choose an appropriate AI interaction pattern—such as inline assistance, copilot, chat, or delegated task—for a specified productivity use case.

Diagram a human–AI collaboration loop that shows user input, AI action, user review, and correction.

Write a structured prompt containing sufficient context, constraints, and output requirements for a productivity task.

Design input scaffolding that helps users provide the context an AI feature needs.

Design expectation-setting cues that communicate an AI feature’s purpose and limitations.

Design an AI output view that distinguishes generated content from supporting evidence and system status.

Design controls that let users review, edit, retry, undo, or reject AI output.

Design interface states that help users recover from uncertainty, low-quality output, and system failure.

Translate a human–AI collaboration loop into a screen-level task flow.

Create a state matrix covering loading, success, uncertainty, empty, and failure conditions for an AI interaction.

Create a representative set of simulated AI responses covering expected, edge-case, and poor-quality outputs.

Build Figma component variants that accommodate differences in AI output length and quality.

Prototype the transitions for reviewing, editing, retrying, accepting, and undoing AI output in Figma.

Write a usability-test protocol that examines comprehension, control, usefulness, and trust.

Conduct a moderated usability test of the Figma prototype and record behavioral evidence.

Create a scoring rubric for evaluating the relevance, accuracy, clarity, and usefulness of simulated AI outputs.

Apply an output-quality rubric consistently to a set of simulated AI responses.

Identify privacy, bias, overreliance, and harmful-action risks in the proposed productivity experience.

Prioritize usability findings and AI-output defects by user impact and frequency.

Revise the Figma prototype to mitigate the highest-priority usability and AI risks.

Document key design decisions and trade-offs using evidence from testing and evaluation.

Structure a portfolio case study that communicates the problem, AI rationale, design process, evidence, and outcome.

Deliver a concise walkthrough of the case study that explains your contribution as an AI product designer.