Split prompts into four layers to keep AI image text intact

A practical frustration is the starting point: AI models reproduce light and texture well, but on-image text tends to come out looking like letters without actually spelling what you asked for. It attributes this to text being discrete — a single wrong character changes the meaning — and to the need to control position and spelling simultaneously. The fix it proposes is structural rather than tool-specific: separate the prompt into subject, shooting conditions, typography, and usage constraints so each concern is stated on its own.

Japanese and Korean have different character density from Latin letters, so writers should put the intended wording directly in the prompt, keep it short, and state the contrast between text and background. It pairs "exact text" with "fully legible / no misspellings" to narrow the room for the model to fall back on letter-like shapes. It also recommends fixing aspect ratio and margins up front, since layout shifts when the ratio is left open.

For corrections, multi-turn editing in a fixed order is favored — subject first, then text, then background and color — with one change per instruction. It points readers to GPT Image 2 as a browser-based environment for testing the same workflow, and stresses that visual inspection, especially zooming in on CJK characters, remains the final check.