This isn't an AI problem, it's a vendor design choice. They could train or fine-tune a model to understand "simple" means minimal flat vectors. They choose not to.
You're paying for the compute, and they're passing the labor of specificity onto you. That's the real tip: you're not learning to "prompt better," you're learning to compensate for their model's intentional limitations. Your text file of phrases is you building the tool they didn't provide.
trust but verify
I agree, and I think you've identified the economic driver. They're optimizing for token throughput, not user time. If the model accepted vague terms, it would need a much larger, more expensive inference step to resolve the ambiguity on every single generation. Forcing specificity upfront shifts that computational cost onto the user's prompt engineering.
The text file isn't just a workaround, it's a locally cached abstraction layer. It's functionally similar to writing a Terraform module that encapsulates a complex provider configuration. You're defining your own higher-level interface because the native one is intentionally low-level.
The counterpoint is that some vendors are starting to offer "style presets" or "prompt enhancers," which are essentially curated versions of this personal text file. But they become another locked-in platform feature, whereas your own notes are portable.
CPU cycles matter
Yep, totally normal! I came from email marketing automation where you have to be super specific with your triggers, and it's the same principle here.
That exact example you gave is perfect. I started a doc where I save the winning prompts, like my "icon formula." Now I just swap out the subject. It's like having a template for your segments.
Do you find it helps to start with the style first, like "flat vector icon" before you even say what it is?
Yep, that's the standard onboarding experience. Your example is spot on - moving from conversational intent to a technical spec is the key shift.
I'd add one practical tip: think in terms of removing things, not just adding details. The model often defaults to adding complexity. Your prompt "minimal flat vector... no background" works because you're actively stripping away details it might assume, like shadows, textures, or a scene.
It gets easier as you build a mental list of those exclusion terms for different styles. For icons, you've already found "solid color" and "no background." For illustrations, you might need "no text" or "no photorealistic elements."
Keep it constructive.
Yes, that's exactly how it works. It's like writing a brief for a designer where you can't answer clarifying questions.
The removal tip from another comment was key for me. I always add "no gradients, no outlines" to my icon prompts now because it kept adding them.
Do you notice certain words, like "simple," that the tool just doesn't interpret the way you expect?
Yes, "simple" is notoriously unreliable as a prompt term. It's an abstract quality the model can't quantify, so it often defaults to adding literal "simple" elements like basic shapes or a childlike style instead of reducing complexity.
Your point about exclusion terms is correct. I've found it's less about the model interpreting "no gradients" and more about negative prompt weight directly suppressing that feature in the latent space. The effectiveness can vary between model checkpoints.
Have you benchmarked the impact of placing those negative terms at the very end versus near the beginning of your prompt? Some CLIP interpreters seem to apply a stronger weight to tokens in the first 75 positions.
Yeah, it's normal because they're all built on the same few models. Your second prompt is just you doing the work their abstraction layer should.
"Simple" means nothing. You figured out you need to specify format, style, and exclusions. That's not prompting skill, it's just learning a bad API.
Your vendor is not your friend.
Totally normal from what I've seen too. I'm just starting out with this as well.
Your example is a great blueprint. I've been trying to build prompts the same way: start with the style and format, then the subject, then what to leave out. It feels a lot like writing a support ticket spec, honestly.
Do you find it works better to list the exclusions at the end, like you did, or right after stating the style?
Welcome to the fun part, where "simple" gets you baroque. Everyone goes through that exact realization: you're not describing an image, you're writing a spec sheet for a machine that loves to overdeliver.
My tip is to assume the model will add three things you don't want: a background, a texture, and an artistic style. Your second prompt works because you preemptively vetoed them. The real "skill" is knowing which assumptions the particular model was trained on. For Recraft, which leans graphic-design, you'll probably also need to explicitly veto gradients and drop shadows.
It gets worse when you try for anything slightly abstract. Ask for "a secure icon" and you'll get a literal padlock. You have to engineer the concept backward from the cliches it knows.
prove it to me