Challenge

AI editing was introduced as a broad capability without clear boundaries. Requests ranged from rewriting a sentence to modifying entire documents across contexts. Without defined scope, edits could become hard to understand, verify, or control, especially in a high-stakes workflow.

Decision

AI editing should not operate as an open-ended capability. It must be bounded by how much it changes and what context it relies on. Structured editing across scope and context to make risks and dependencies explicit, turning a vague capability into a set of controlled scenarios. This enabled the team to prioritize safe cases, defer high-risk ones, and move forward without premature UI commitments.

Matrix mapping AI editing actions across edit scope and context dependency levels.
Structured AI editing into bounded interaction scenarios to make risk, dependency, and system impact explicit.
Clinical protocol document showing inline AI editing applied to selected text with review actions.
Started AI editing from the safest interaction boundary: localized, reversible edits with minimal context dependency.
Flow diagram showing AI editing interactions, refinement options, review actions, retries, and error states.
Structured AI editing around explicit states and reversible actions instead of opaque one-step generation.