Vibes prompts are a gamble.
“Make a nice IG card about X” works maybe half the time in ChatGPT Images 2.0. The other half you’re regenerating and hoping the model guesses better on the second try. Sometimes it does. Often it doesn’t. You end up burning 10 minutes on a task that should take 90 seconds, and the image you land on is still a compromise.
u/israynotarray spent a weekend stress-testing gpt-image-2 and found the pattern: every prompt that consistently produced something usable shared the same six-field structure. Every flaky result skipped one or more of those fields. Not a coincidence. A repeatable finding across dozens of test runs and multiple content types.
The Contrast Worth Understanding
The vibes approach: describe what you want in one loose sentence. The model guesses at layout, picks its own palette, and sometimes paraphrases your on-image text into something close but wrong. “Close but wrong” is the killer. A headline that says “How APIs Connect Your Apps” instead of “What is an API?” looks fine at a glance and breaks your content the moment someone reads it carefully.
The structured approach: break the brief into six explicit fields. The model fills in nothing it wasn’t told to fill in. There’s no guesswork about whether you wanted a 4:5 or a square crop, whether the accent color is orange or gold, whether the three bullet points should read exactly as written or get smoothed into something “cleaner.” You wrote it down. The model follows it. That’s the whole difference.
Think of it like a design brief vs a Slack message. Both communicate intent. One produces predictable output.
🗂 The Six Fields
- Subject, the core message, what the card is actually teaching or saying
- Layout, orientation, ratio, how zones split across the canvas
- Palette, main color, accent, background, no more than three values
- Typography, title style, visual hierarchy, whether body copy is light or regular weight
- On-image text, exact strings, wrapped in quotes, labeled as title/subtitle/bullet
- Style, flat, illustrated, photographic, minimal, editorial, etc.
Field five is where most prompts break. If you don’t quote the on-image text, the model paraphrases. It’s not being difficult. It’s doing what language models do: inferring intent and producing a reasonable interpretation. Quote the text explicitly and label each piece, and it reproduces exactly what you wrote. The word “title” before a quoted string tells the model this is fixed copy, not a description of tone.
Field two trips people up in a different way. Skipping layout doesn’t mean no layout. It means the model picks one. Sometimes that’s fine. When you’re building a consistent content series, it never is.
A Working Template You Can Copy
This came from the original test and holds up across content types:
Draw a portrait IG card (4:5 ratio) on the topic ‘What is an API? in 3 minutes’. Layout in four zones: top headline takes 1/5, middle has three rounded cards each holding one key point, bottom has whitespace for a signature. Color: off-white background, deep blue title, orange accents. Typography: title in bold sans-serif, body in regular sans-serif. On-image text: title ‘What is an API?’, subtitle ‘In 3 Minutes’, three points reading ‘A bridge between programs’, ‘Moves data from A to B’, ‘How frontend and backend work together’. Style: flat design, line-icon illustrations, lots of whitespace.
Swap the bracketed parts for your own topic. Keep everything else as a base and adjust palette or style for your brand. The zone structure in the layout field is doing more work than it looks like. It tells the model where to put visual weight and where to leave breathing room, which is most of what makes a card feel designed rather than generated.
Once you have a template that works for one content type, duplicate it and change only what needs to change for the next one. You’re building a prompt library, not crafting a new brief from scratch every time.
⚡ Two Time-Savers From the Testing
- Edit mode for small fixes. If one or two characters render wrong, don’t regenerate the whole image. Ask edit mode to fix just that region instead. Much faster, and it preserves everything else that rendered correctly. This is especially useful when the layout and palette are exactly right but one word in the subtitle got dropped or garbled.
- Lock the ratio upfront. 4:5 for IG cards, 16:9 for blog headers, 3:4 for magazine style. Leave it open and the model picks something arbitrary. Arbitrary ratios mean manual cropping, and manual cropping means you’re back in Figma for a task you were trying to skip.
On the Text Rendering Improvement
Chinese, Japanese, and Korean text now comes out readable in gpt-image-2. That used to be the main reason to give up and open Figma instead. Multi-language content teams were basically locked out of AI image generation for anything that needed on-image copy. That pain point is mostly gone, as long as you’re using the structured format. Vague prompts still produce garbled CJK output. The six-field structure gives the model enough context to treat the text as fixed copy rather than visual texture.
The original post goes deeper with 30 templates across different use cases: knowledge decks, carousel covers, blog headers, infographics, product mockups, before-and-after cards, quote cards, habit trackers. Worth bookmarking if you’re producing visual content at volume. Thirty templates sounds like a lot until you realize most of them share the same skeleton and differ by ratio, palette, and layout zones.
The model isn’t flaky. Vague prompts are flaky. Give it a real brief and it delivers a real result.
Spent a weekend with ChatGPT Images 2.0 — the prompt structure that actually produces usable designs (working template inside)
by u/israynotarray in ChatGPTPromptGenius