Career proof-of-work

Build AI Artifacts You Can Actually Show

Certificates and tool names help. Proof helps more. Use these templates to turn AI-assisted work into resume bullets, interview stories, portfolio examples, and safer workplace outputs.

Start with a case study Fix resume wording

Resume Proof Bullet

Convert vague AI claims into honest, specific achievement bullets.

Portfolio Case Study

Document one workflow with inputs, prompts, output, and human review.

Workflow Audit

Find three tasks worth improving with AI this week.

AI Output QA Checklist

Catch hallucinations, weak claims, privacy issues, and tone problems.

Role Briefs

Prompts and artifacts for marketing, analytics, product, data science, and writing.

Weekly Proof Log

Keep a lightweight record of what you tried, saved, and verified.

Artifact 1

Resume proof bullet

Weak AI resume claims sound like this: "Used ChatGPT to improve productivity." Strong claims name the workflow, the judgment, and the result.

Before

Used AI tools to create reports and improve team efficiency.

After

Built a weekly reporting workflow using ChatGPT to summarize variance drivers, then verified outputs against dashboard totals; reduced first-draft reporting time from 3 hours to 55 minutes.

Template

Used [AI tool] to [specific task], verified by [human check/source], resulting in [time saved, quality improvement, output created, or business result].

Read the resume guide

Artifact 2

One-page AI portfolio case study

A good AI portfolio piece is not a screenshot of a chatbot. It is a decision record: the problem, the inputs, the prompt pattern, the output, what you rejected, and what changed after human review.

  1. Problem: What recurring task or business question did you work on?
  2. Inputs: What data, notes, job description, customer feedback, or constraints did you provide?
  3. Prompt pattern: What role, steps, format, and quality bar did you give the model?
  4. Output: What draft, analysis, plan, or artifact did AI produce?
  5. Human review: What did you verify, delete, rewrite, or escalate?
  6. Result: What got faster, clearer, safer, or easier to explain?

Keep the case study under one page. If you cannot explain it in two minutes, it is not ready for an interview.

Artifact 3

The AI workflow audit

Do not automate the most visible task first. Audit for tasks that are frequent, text-heavy, low-risk, and easy to verify.

Good first targets

Meeting summaries, draft briefs, dashboard narratives, email variants, first-pass research, interview prep.

Avoid first

Legal claims, final numbers, confidential data, medical or financial advice, hiring decisions.

Proof to collect

Time before/after, error caught, clearer output, reusable template, manager feedback.

Artifact 4

AI output QA checklist

  • Can I trace every number, claim, and source?
  • Did the model invent a metric, quote, feature, or policy?
  • Would I be comfortable explaining how this was produced?
  • Does the wording sound like me, my company, or a generic LinkedIn post?
  • Did I remove private data, customer details, and sensitive internal context?
  • Does this need legal, manager, or subject-matter review before use?

See this week's risk check

Artifact 5

Role briefs that prove applied skill

Each role should produce a different proof artifact. A marketer should not show the same AI artifact as a data scientist.

Marketing

Campaign brief with audience, offer, test plan, and claims review.

Analytics

Dashboard narrative with metric definition, caveats, and follow-up questions.

Product

PRD section with assumptions, edge cases, acceptance criteria, and telemetry.

Copywriting

Before/after rewrite with voice controls, proof, and compliance notes.

Artifact 6

Weekly proof log

Once a week, write down one AI workflow you tried, what it changed, what you verified, and whether you would use it again. After eight weeks, you have better interview stories than someone who only lists "prompt engineering" in a skills section.

Use the blog for weekly prompts Open prompt library