Set a quality and verification bar

For choose and run a two-week AI career experiment instead of guessing a forever title, define quality across accuracy, completeness, usefulness, safety, and reproducibility. Weight dimensions according to harm. A cosmetic miss can be revised; an unsupported claim, broken calculation, privacy leak, or rights violation blocks release.

Translate each dimension into observable checks. Accuracy means a claim, value, behavior, or frame agrees with an authoritative source. Completeness means every required field or stage appears. Usefulness means a teacher exploring product, operations, data, engineering, or governance work can take the intended action. Safety includes the boundary that you must remove employer-confidential examples, student data, contact details, and sensitive employment history before sharing career materials. Reproducibility means the prompt, input version, settings, and review evidence are saved.

Use checks from cheapest to strongest:

1. Contract check: required sections, schema, length, and prohibited content.
2. Source check: trace claims and values to supplied evidence.
3. Edge check: run normal, boundary, missing, and adversarial cases.
4. Independent check: calculate, test, rehearse, listen, inspect, or open the original.
5. Human gate: a responsible reviewer approves consequential use.

In this chapter, the concrete verification is to collect five current job descriptions per path, count repeated requirements, interview a practitioner, inspect current labor sources, and evaluate whether the work itself fits. The expected candidate is Three bounded experiments across AI education, product operations, and evaluation, each producing evidence rather than promising a job outcome. Record actual evidence, not a checkbox copied from the prompt.

Score each criterion 0 (fails), 1 (partly), or 2 (passes). Any zero for factual correctness, permission, privacy, or required disclosure is an automatic stop. A total score is useful for comparing iterations, but it must never average away a blocking defect.

I enjoy explaining hard ideas, interviewing people, and editing. Skills: classroom teaching, spreadsheets, basic research. Constraint: four hours weekly and no career break. Suggest three role-family experiments. For each: transferable evidence, one gap, a two-week portfolio artifact, and one professional to learn from. Do not predict salary or hiring probability without current sources.

After generation, sample beyond the happy path. Failures such as chasing trendy titles; fabricated salary claims; treating course completion as evidence; ignoring domain expertise; building projects no target role values often survive a superficial review because the output has the right shape. Use a counterexample designed to expose the riskiest assumption.

Store the rubric result, reviewer, date, input version, failed cases, and unresolved limitations. If the artifact changes, rerun affected checks. Titles change faster than durable work. Optimize for evidence that you can perform a useful task, explain trade-offs, and learn from feedback. Quality assurance is part of the work, not an apology added at the end.