Most AI image tools are great at scenes and weak at words. Headlines come out misspelled, prices land in the wrong corner, and a bilingual menu turns into gibberish. If your design needs accurate text, treat the prompt like a layout brief, not a mood board.
1. Write the copy offline first
List every line that must appear: headline, date, venue, price, dish names, Chinese characters next to English. Spell each one exactly as it should print. Generators invent filler when you leave gaps.
2. Quote every required word
Put required text in quotes inside the prompt. Anything unquoted may be improvised. Keep each block short — two to six words for a headline works better than a long sentence wrapped around a poster edge.
3. Place text on purpose
If your tool supports layout boxes, drag a box for the headline, another for the date, and another for the call to action. Cramped boxes produce squeezed type. Empty space without a "no other text" rule often fills with fake copy.
4. Test unusual names and a second language
Common English words are an easy pass. Odd brand names, prices, and a second script (for example a Chinese dish name next to its English translation) expose failures fast. That small test set is more useful than a generic Elo score when your product puts words on the image.
5. Proofread at full size, then recheck after edits
Read every word. After you fix one typo by regenerating or editing, re-check the parts you did not touch — changing one word can quietly alter a price elsewhere.
6. Treat AI output as a strong mockup
Use the image to choose style and hierarchy. For anything you will print or ship, set final type carefully, or use a tool that lets you edit text after generation so a date change does not force a full redraw.
Tools focused on lettering accuracy, such as MOSS-Image AI, which handles English and Chinese text and lets you drag boxes to place headlines and prices, make this workflow much closer to a finished design. The checklist above still applies: quote the words, place them, and proofread.
I work on MOSS-Image AI. We published a small text-accuracy study (8 scenes, 131 images) if you want to see where this approach helps and where it still struggles.
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