Every job listing seems to mention AI literacy now, but if you’re not a developer, it’s genuinely unclear how you’re supposed to demonstrate that you understand this stuff. Putting “familiar with ChatGPT” on your resume isn’t going to cut it, but neither is pretending you can code neural networks.

I’ve reviewed about 200 portfolios in the last six months as part of hiring for various roles. Here’s what actually works when you’re trying to show AI competency without technical credentials.

Show How You’ve Applied AI Tools to Real Problems

This is the big one. Don’t tell me you “use AI tools”—show me specific problems you’ve solved with them.

A marketing manager I hired recently had a portfolio case study titled “How I Cut Campaign Research Time by 60% Using AI Analysis.” She’d taken a typical campaign planning process, identified where AI could help (competitive analysis, audience research, content ideation), and documented the before/after workflow.

The portfolio included:

• The specific prompts she used
• Screenshots of outputs
• Her evaluation of what worked and what didn’t
• Actual business results (time saved, campaign performance)

She’s not technical. She can’t explain how transformers work. But she demonstrated something more valuable: judgment about when and how to apply AI tools effectively.

This beats any certificate course listing on a resume. It shows you’ve actually done the work.

Document Your Prompt Engineering Process

Prompt engineering is a real skill, and it’s one you can demonstrate easily. If you’re using AI tools regularly in your work, you’ve probably developed techniques for getting better outputs.

Document that. Create a portfolio section showing:

• A challenging task you needed to accomplish
• Your iterative prompting approach
• Examples of poor outputs and how you refined the prompts
• The final result and why it worked

I talked to Team400’s AI team about what they look for when hiring non-technical AI roles, and they specifically mentioned this. Someone who understands how to structure prompts, iterate based on outputs, and combine AI-generated content with human judgment is valuable across industries.

Create Before/After Case Studies

Pick 2-3 substantial projects where you used AI tools and create detailed before/after documentation.

Before AI:

• What was the process?
• How long did it take?
• What were the pain points?

After AI implementation:

• What changed in the workflow?
• What tools did you use and why?
• What results improved?
• What didn’t work as expected?

The “what didn’t work” section is crucial. It shows you understand AI’s limitations, which is often more important than knowing its capabilities.

A content strategist I know created a case study about using AI for SEO research. She was honest about where the AI-generated keyword suggestions were useful and where they were nonsense. That critical thinking is exactly what employers want to see.

Build Your “AI Tools Stack” Documentation

Create a simple reference showing which AI tools you use for which purposes. This demonstrates breadth of knowledge and practical application.

For example:

• Research & analysis: Perplexity for initial research, Claude for synthesizing sources
• Content drafting: ChatGPT for first drafts, Grammarly for editing
• Visual work: Midjourney for concept imagery, Canva’s AI features for design refinement
• Data work: ChatGPT for Excel formula assistance, basic data cleanup

Add brief notes on why you choose each tool for specific tasks. This shows you’re making informed decisions, not just using whatever’s trendy.

Show Cross-Functional AI Application

If you work across different areas, demonstrate how you’ve adapted AI tools to various contexts.

A project manager’s portfolio I reviewed recently included examples of using AI for:

• Meeting summaries and action item extraction
• Risk analysis on project proposals
• Schedule optimisation suggestions
• Stakeholder communication drafting

None of this required technical skills. All of it showed sophisticated thinking about where AI adds value to existing workflows.

Include “AI Audits” of Your Own Work

This is less common but genuinely impressive: audit your own AI usage and document what you learned.

After using AI tools for a quarter, analyze:

• Which applications saved meaningful time?
• Which were more trouble than they’re worth?
• Where did AI outputs need significant human revision?
• What patterns emerged in successful vs unsuccessful AI applications?

This meta-level thinking demonstrates maturity in AI adoption. You’re not just using tools—you’re evaluating their effectiveness critically.

Don’t Fake Technical Depth

Do not pretend you understand machine learning architecture if you don’t. Do not put “AI/ML” as a skill if you mean “I use ChatGPT sometimes.”

Be specific and honest about your level:

• “AI tool power user” is legitimate
• “Experienced in prompt engineering for content work” is clear
• “Familiar with practical AI applications in marketing” is honest

These are valuable skills. You don’t need to oversell them with technical jargon you don’t understand.

Create a “Learning Journey” Section

If you’re actively developing AI skills, document that process. This is especially good for people transitioning into new roles.

Show:

• Courses or resources you’re working through
• Experiments you’re running with different tools
• Concepts you found confusing and how you figured them out
• Projects you’re building to practice

This demonstrates growth mindset and self-directed learning, which matters more than current skill level for many roles.

The Practical Portfolio Structure

Here’s a simple structure that works:

1. Overview: Brief statement of your AI literacy level and focus areas
2. Case Studies: 2-3 detailed examples of AI application in real projects
3. Tools & Methods: Your current AI toolkit and how you use each tool
4. Learning & Development: What you’re currently developing
5. Reflections: What you’ve learned about effective AI use

Keep it scannable. Use screenshots. Be specific about results.

What Actually Matters

Employers aren’t looking for everyone to become data scientists. They’re looking for people who can:

• Identify opportunities for AI application
• Use tools effectively to solve real problems
• Evaluate outputs critically
• Integrate AI into existing workflows thoughtfully

You can demonstrate all of that without writing a line of code.

The people who’ll thrive in AI-augmented work aren’t necessarily the most technical. They’re the ones who combine domain expertise with good judgment about when and how to apply new tools.

Document that judgment. Show your work. Be honest about your level. That’s a portfolio that’ll actually get you hired.

For more on AI literacy frameworks, check out MIT’s AI education resources and the Australian Government’s AI skills framework.