Workplace AI reference
AI Output Review Checkpoints
AI output should be treated as draft support. Before it becomes workplace communication, documentation, analysis, or process guidance, it needs human review.
Review before workplace use
These checkpoints help separate useful AI assistance from output that is fluent but incomplete, inaccurate, unsafe, or outside the user's authority.
- Facts: Are names, dates, numbers, requirements, and source details accurate?
- Sources: Does the answer cite or rely on the right policy, document, ticket, record, or public source?
- Missing information: Did the AI label gaps instead of filling them in?
- Tone and audience: Does the output fit the recipient, workplace context, and level of formality?
- Privacy: Did the prompt or output include sensitive, confidential, regulated, customer, employee, credential, or financial details?
- Authority: Does a manager, policy owner, legal, HR, finance, IT security, safety, or compliance reviewer need to approve it?
How this connects to labs
AI Lunchroom labs ask learners to bring AI output back for review because the skill is not only writing a prompt. The skill is deciding whether the result is ready, needs revision, or needs escalation.
- Starter learners check simple outputs for clarity, missing information, and oversharing.
- Operator learners review everyday drafts, checklists, summaries, and handoffs.
- Builder learners test reusable prompts and templates for repeatable quality.
- Lead learners define team review habits, escalation rules, and safe-use standards.