Your weekly AI habit
Set a quality and verification bar
Quality is a rubric plus independent evidence, not confidence in a polished answer.
Before you start
Why this matters
Without opening an AI tool, write the acceptance test for this job: run a thirty-minute weekly practice loop that improves one real workflow. Name one fact that must be exact, one judgment a person must make, and one condition that should stop the workflow. Compare your answer with the professional standard below; the gap is what you should practice.
1Learn the idea
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Set the bar before generation
For run a thirty-minute weekly practice loop that improves one real workflow, 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 busy learner who wants durable skill rather than tool chasing can take the intended action. Safety includes the boundary that you must practice with synthetic or redacted material; keep a standing list of data that must never enter consumer tools and review vendor settings. Reproducibility means the prompt, input version, settings, and review evidence are saved.
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Verification ladder
Use checks from cheapest to strongest:
- Contract check: required sections, schema, length, and prohibited content.
- Source check: trace claims and values to supplied evidence.
- Edge check: run normal, boundary, missing, and adversarial cases.
- Independent check: calculate, test, rehearse, listen, inspect, or open the original.
- Human gate: a responsible reviewer approves consequential use.
In this chapter, the concrete verification is to compare against the baseline, verify every owner and due date against source notes, record failure cases, and repeat on a second sanitized example before adopting. The expected candidate is A bounded weekly experiment with a baseline, one controlled change, an output check, and a decision to keep, revise, or discard the technique. Record actual evidence, not a checkbox copied from the prompt.
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A scoring rubric
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.
This week I want to improve meeting-note follow-up. Baseline: 25 minutes and occasional missed owners. Design one 30-minute practice: a sanitized sample, one constrained prompt, a checklist for owner/date/source accuracy, and a five-minute retrospective. Keep the tool fixed and change only one prompting variable.
After generation, sample beyond the happy path. Failures such as collecting prompts without testing; changing tool and task simultaneously; counting speed while quality falls; skipping reflection; automating before understanding often survive a superficial review because the output has the right shape. Use a counterexample designed to expose the riskiest assumption.
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Release evidence
Store the rubric result, reviewer, date, input version, failed cases, and unresolved limitations. If the artifact changes, rerun affected checks. Consistency beats novelty. One checked experiment each week produces transferable judgment; browsing new tools without measuring work does not. Quality assurance is part of the work, not an apology added at the end.
Continue learning · glossary & guides
- Which criterion cannot be traded off against a high total score?
- What independent evidence would prove the candidate works in context?
- Reference · Related concept
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