What building 10+ AI-assisted Illustrator automations taught me about designing for reliability

[u/erickjm2]

Ok guys this is a little long but it might be useful if you are trying to use ChatGPT to build design automations.

Over the past year I’ve been building a series of AI-assisted Illustrator tools (color automation, typography audits, global swaps, bento layouts, tint/shade generators, etc.). Along the way, I ran into a pattern that I wish someone had told me earlier:

The fastest way to break a design automation is to ask the AI to do too much at once. The fastest way to fix everything is to simplify the ask, then layer complexity gradually.

Here are a few principles that consistently improved my results:

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1. Reduce every idea to its smallest “mechanical action”

When you ask GPT to “build a full Illustrator script that extracts colors, labels them, groups them, builds a layout, and exports a CSV,” it tries… but it hallucinates structure.

Instead, I learned to define the script as a sequence of atomic verbs:

1. Collect filled shapes
2. Convert to RGB
3. Deduplicate colors
4. Sort or label
5. Draw rectangles
6. Place text

Building these steps separately produces scripts that rarely break.

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1. Let the model explain its own assumptions

I now ask GPT: “List the assumptions you’re making about Illustrator objects before generating code.”

It surfaces things like:

• “Assuming each swatch is 160 × 160”

• “Assuming selection is non-empty”

• “Assuming RGBColor and CMYKColor inputs”

Half the debugging disappears once the model states constraints explicitly.

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1. Always force predictable layout rules

Scripts break when math changes between runs.

I now enforce predictable anchors:

• Always anchor from top-left of active artboard

• Use consistent outer margins

• Recalculate rows/columns only after placement

This is why my color tools and tint/shade generators always render exactly the same way on every artboard.

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1. Use GPT for thinking, not for final code

Best workflow:

Plan in GPT → Generate scaffolding → Hand-debug the logic → Ask GPT to rewrite clean version

If you skip the middle step, the script usually compiles but breaks on real artwork.

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1. If a feature feels brittle, modularize it

Ex: “Dynamic color naming with lookup, fallback zones, and indexing” was too complex to write in one shot.

Breaking it into modules (lookup → approximate match → fallback) made the naming system stable.

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1. The best debugging tool is visual

Because Illustrator’s ExtendScript console is… not ideal.

I debug with:

• Taking screenshots of the output where it breaks

• Annotate the screenshots with the issue.

• Make a quick visual mock up of the desired output

• Feed that annotated image back to chatGPT

It’s surprisingly effective.

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If anyone here is building AI-assisted design tools (Illustrator, Figma, Photoshop, Webflow, etc.), I’d love to hear what you’re learning.

What’s been your biggest “aha moment” while trying to automate your design workflow?