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To prevent AI from making assumptions on ambiguous requests, enforce a question-first workflow requiring the system to ask clarifying questions and confirm intent before taking action. This stops scope creep by documenting boundaries upfront.

A pre-execution audit in AI task workflows is a verification step that reviews and documents system boundaries before any action is taken. It ensures understanding is validated against user intent, guarding against unsafe actions and scope creep.

You can add AI guardrails for modifying files by implementing a strict understand, clarify, confirm, and audit pipeline. This enforces explicit user confirmation and boundary checks before the AI executes any file modifications or system tasks.

Enforcing a clarify-confirm-execute cycle introduces a deliberate verification phase before task execution. While it adds an initial clarification step, it prevents costly errors and unsafe actions by ensuring the AI fully understands the instruction boundaries first.

You should use a question-first workflow for AI instructions whenever a user request is ambiguous, involves changes to files or systems, or triggers slash-commands where understanding must be verified to prevent overreach and unsafe actions.

AI fills ambiguity with assumptions because it attempts to complete tasks without explicit boundaries, leading to scope creep and unsafe actions. Implementing a visible pre-execution audit forces the system to ask clarifying questions instead of guessing intent.