Testing Visual Documentation: Making Sure Your AI Visuals Actually Work

Alex spent three weeks creating the perfect diagram of his team’s AI recommendation system. It was a work of art—colorful, comprehensive, with perfect layout and typography. He proudly added it to the documentation and waited for the praise to roll in.

Instead, he got confusion. “What does this arrow mean?” “I don’t understand how the data flows.” “Which part represents the model training?”

The painful truth hit him: a visualization that makes perfect sense to its creator might be completely baffling to everyone else.

Sound familiar? You’re not alone. We’ve all been Alex at some point, creating visuals that make perfect sense in our heads but confuse our audience. The good news? With the right testing approach, you can catch these issues before they cause problems.

In this module, we’ll explore how to test your visual documentation to ensure it actually accomplishes what you need: transferring understanding from your brain to someone else’s.

Why Even Beautiful Visualizations Can Fail Miserably

Let’s start with a simple truth: your brain is playing tricks on you. When you create a visualization, you bring along all your background knowledge and context. You see things in your diagram that others literally cannot see.

This phenomenon has a name: the curse of knowledge. Once you know something, it’s nearly impossible to imagine what it’s like not to know it. This creates a massive blind spot when creating visualizations for others.

Common visualization failures include:

• Assumed knowledge gaps: “Obviously everyone knows what a transformer architecture is!” (Narrator: They don’t.)
• Unclear flow: Arrows pointing everywhere like a plate of visual spaghetti
• Symbol confusion: Using icons or symbols that make sense to you but are cryptic to others
• Cognitive overload: Cramming so much information into one diagram that viewers don’t know where to look
• Missing context: Failing to explain what the visualization is showing and why it matters

The truth is, we’re all terrible judges of our own visualizations’ clarity. That’s why testing isn’t optional—it’s essential.

The Visual Testing Toolkit: 5 Methods to Validate Your Diagrams

1. The “Fresh Eyes” Test: Getting Immediate Reactions

This is your first line of defense against unclear visualizations. Show your diagram to someone who hasn’t seen it before and ask:

What to do:

1. Find a colleague who isn’t familiar with your visualization
2. Show them the image for 30 seconds (no more!)
3. Take it away and ask: “What do you think this is showing?”
4. Listen carefully without interrupting or defending your work

What you’ll learn:

• Whether the main point is immediately clear
• What elements draw attention first (which might not be what you intended)
• What generates confusion or questions

The reality check: If they can’t explain the main point back to you in simple terms, your visualization needs work—no matter how beautiful or technically accurate it is.

2. The “Five Second” Test: Testing Immediate Comprehension

This test measures whether viewers can grasp the key information in your visualization almost instantly.

What to do:

1. Show your visualization to a tester for exactly five seconds
2. Hide it and ask a specific question: “What was the relationship between component A and component B?” or “What was the main trend shown?”

What you’ll learn:

• Whether key relationships are visually obvious
• If important information stands out appropriately
• How well your visual hierarchy (what’s emphasized) works

Quick tip: Use a free online tool like UsabilityHub to run this test with multiple people remotely.

3. The “Describe and Draw” Test: Checking for Mental Model Transfer

This powerful test reveals whether your visualization successfully transfers your mental model to the viewer.

What to do:

1. Show your visualization to someone for 60 seconds
2. Take it away and give them a blank piece of paper
3. Ask them to draw what they remember, focusing on the relationships and structure (not artistic quality!)
4. Compare their drawing to your original

What you’ll learn:

• Which elements were memorable enough to be reproduced
• How the relationships between components were understood
• What was completely missed or misunderstood

The humbling truth: Prepare to be surprised by what people actually take away from your visualization versus what you thought you were communicating!

4. The “Prediction Test”: Validating Understanding Through Questions

This test checks whether viewers can use your visualization to reason about the system it represents.

What to do:

1. Show your visualization and ask “what if” questions:
   • “What would happen in the system if component X failed?”
   • “How would the data flow change if we added another step here?”
   • “Which part would need to change if we wanted to add feature Z?”

What you’ll learn:

• Whether your visualization conveys the system’s behavior, not just its structure
• If cause-and-effect relationships are clear
• How well viewers can use the visualization to reason about changes

Make it concrete: For an AI system flowchart, you might ask: “If we wanted to add more training data, where would it enter the system?”

5. The “Expert vs. Novice” Test: Ensuring Appropriateness for Your Audience

Different audiences need different levels of detail. This test ensures your visualization works for its intended audience.

What to do:

1. Show your visualization to both an expert in the subject and a relative novice
2. Ask both the same questions about what they understand
3. Compare their responses

What you’ll learn:

• Whether your visualization is too technical or too simplified
• If you’ve found the right balance for your target audience
• How to adjust based on who will actually use the documentation

The balancing act: If experts say “this is oversimplified” but novices say “I can’t follow this,” you might need multiple visualizations for different audience levels.

The Testing Process: From Creation to Validation

Let’s walk through a practical process for incorporating testing into your visual documentation workflow:

1. Test Early with Low-Fidelity Mockups

Don’t wait until you’ve created a polished visualization to start testing. Begin with simple sketches.

Why this works:

• People are more comfortable giving honest feedback on something that looks unfinished
• You’ll waste less time redoing polished work
• You can test multiple approaches quickly

Pro tip: Even a paper sketch or whiteboard drawing can be tested. Take a photo and show it to colleagues for quick reactions.

2. Incorporate Feedback Systematically

Develop a system for collecting and acting on visualization feedback.

Simple feedback template:

• What was clear about this visualization?
• What was confusing or raised questions?
• What information seemed to be missing?
• What felt unnecessary or distracting?

The documentation hack: Keep a “visualization feedback log” where you record common issues. You’ll start seeing patterns that will improve all your future visualizations.

3. Test with Your Actual Target Audience

Whenever possible, test with people who actually represent your end users.

Who to include:

• New team members (great proxies for external developers)
• People from adjacent teams who need to understand your system
• Actual customers or users (if you can access them)
• People with different technical backgrounds

Reality check: Your fellow AI engineers are almost certainly not representative of most of your documentation users. They know too much!

4. Check for Accessibility Issues

Make sure your visualizations work for everyone, including people with color blindness or other visual impairments.

Quick checks:

• Run your visualization through a color blindness simulator like Coblis
• Ensure text within visualizations has sufficient contrast
• Check that information isn’t conveyed by color alone

The simple test: Convert your image to grayscale. If you lose important information, you need to fix it.

5. Iterate Based on Testing Results

Testing isn’t a one-time event. Plan for multiple rounds of improvement.

Iterative testing checklist:

• Make one major change at a time so you can tell what improved
• Test with fresh participants (previous testers are now “contaminated” with knowledge)
• Document what changed and how results improved
• Know when to stop—perfect is the enemy of good enough!

Common Problems and Their Visual Solutions

Based on thousands of tested visualizations, here are the most common problems that emerge during testing and how to fix them:

Problem: “I don’t know where to start looking”

Solution: Add a clear visual entry point, usually at the top left for Western readers. Use size, color, or position to create a clear starting point for the viewer’s journey through your visualization.

Problem: “I don’t understand what these arrows mean”

Solution: Be consistent with connector meanings. Consider adding a small legend explaining different types of connections (data flow, control flow, dependencies).

Problem: “This is too complicated to understand”

Solution: Layer your information. Start with a simplified high-level view, then allow viewers to “drill down” into more complex layers, either through interactive elements or through a series of increasingly detailed visualizations.

Problem: “I can’t tell which parts are most important”

Solution: Use visual hierarchy principles. The most important elements should be:

• Larger
• More colorful/higher contrast
• More centrally located
• Surrounded by more white space

Problem: “I don’t see how this relates to what I’m trying to do”

Solution: Add practical examples or use cases directly in your visualization. Show how the diagram connects to real-world usage.

Case Study: Before and After Testing

Let’s look at a real example of how testing transformed an AI system visualization:

The original diagram was a complex flowchart of a machine learning pipeline that the team thought was crystal clear. It showed every component of the system with careful labeling.

The test results were eye-opening:

• 7 out of 8 testers couldn’t identify the overall purpose of the system
• Most couldn’t trace the path of data through the system
• Several mistook training components for inference components

The redesign broke the visualization into three simpler diagrams:

1. A high-level overview showing the major system components
2. A detailed training pipeline visualization
3. A separate inference pipeline visualization

The results after redesign:

• 90% of testers could correctly describe the system’s purpose
• Users could accurately trace data flows through each subsystem
• New team members reported using the diagrams as reference during their first weeks

The lesson: Sometimes the best fix for a confusing visualization is to break it into multiple, simpler visualizations with a clear narrative connecting them.

DIY User Testing Exercise: Test Your Own Visualization

Time to put these principles into practice with your own visualization:

The mission: Run a simple test on one of your existing AI system visualizations (or create a new one if needed).

Your testing plan:

1. Select a visualization from your documentation
2. Find 2-3 people who aren’t familiar with it (colleagues from other teams work great)
3. Run the “Fresh Eyes” test described above
4. Document what you learn
5. Make at least one improvement based on the feedback
6. Test again to see if comprehension improves

Reflection questions:

• Were you surprised by what people did or didn’t understand?
• What assumptions had you made that weren’t obvious to others?
• How might this impact your approach to future visualizations?

Advanced Testing Approaches: When Stakes Are High

For critical documentation where clarity is essential, consider these more formal testing approaches:

Eye-Tracking Analysis

Eye-tracking technology shows exactly where people look when viewing your visualization and in what order.

When to use it: For extremely important documentation where you need to ensure users follow a specific visual path to understand complex systems.

How to do it simply: While professional eye-tracking is expensive, you can approximate it by having someone talk through what they’re looking at in real-time as they view your visualization.

A/B Testing Different Approaches

When you have multiple possible visualization approaches, test them against each other.

Simple method:

1. Create two different visualizations of the same information
2. Show each to a similar group of testers
3. Ask the same questions of both groups
4. Compare comprehension rates and accuracy of understanding

The data advantage: This gives you quantifiable data about which approach works better, not just subjective opinions.

Longitudinal Testing: The Long-Term Memory Test

This test checks whether your visualization creates lasting understanding.

How it works:

1. Show testers your visualization and ask comprehension questions
2. One week later, ask the same questions without showing the visualization again
3. See how much they’ve retained

Why it matters: Sometimes visualizations that seem clear in the moment don’t create lasting mental models—this test reveals which explanations stick.

Resources to Deepen Your Testing Toolkit

Books and Articles

• Don’t Make Me Think by Steve Krug - The classic on usability testing
• Visualizing Data by Andy Kirk - Includes excellent sections on evaluation
• User Testing Visual Design by Nielsen Norman Group

Testing Tools

• UsabilityHub - Run remote five-second tests and more
• Optimal Workshop - Tools for testing information architecture
• Maze - User testing platform with visual reporting

Communities for Feedback

• UX Stack Exchange - Get expert feedback on visualization approaches
• DataViz Society - Community of data visualization professionals
• Information is Beautiful Awards - See exemplary visualizations that have been thoroughly tested

Wrapping Up: From Testing to Excellence

Testing your visualizations isn’t just about avoiding embarrassing mistakes—it’s about developing a fundamental skill: the ability to see your work through others’ eyes.

With each test, you’ll build stronger instincts for what works and what doesn’t. You’ll start anticipating confusion points before they happen. You’ll develop a sixth sense for clarity.

Remember: the goal isn’t perfect visualizations (there’s no such thing). The goal is visualizations that successfully transfer understanding to your specific audience with minimal friction.

In our next module, we’ll explore the broader process of documentation review, building on these testing principles to ensure your entire documentation system works together seamlessly.

As the visualization expert Alberto Cairo puts it: “A good visualization isn’t about showing the data—it’s about enabling understanding.” With the testing approaches in this module, you’re now equipped to ensure your visualizations do exactly that, turning complex AI concepts into clear, accessible knowledge for everyone who needs it.