Instagram carousel slide 1 — quote-driven hook

Why this prompt works on GPT Image 2

This prompt is engineered for creators on Xiaohongshu (RedNote), Instagram, TikTok, Pinterest. The phrasing prioritizes survive the 0.3-second scroll-stop test and earn a save / repin — every word in the prompt is doing one of three jobs: setting subject, controlling lighting / camera, or constraining style. What makes it land specifically on gpt-image-2: the model parses noun-clause phrasing (e.g. "centered on pure white seamless background") more reliably than vague style adjectives ("clean look"). The version above leans heavily on noun clauses — this is intentional. The Chinese "小红书 (Xiaohongshu)" cover convention is dramatically different from Western Instagram aesthetics — explicit aspect-ratio and font choice is critical.

5 variations to try with this prompt

1. Swap the subject — replace any [PRODUCT] or [SUBJECT] placeholder with your specific item. Be concrete: "matte navy ceramic mug" beats "a mug".
2. Change lighting language — try "golden hour rim lighting" or "harsh overhead studio fluorescent" instead of "soft studio lighting" to dramatically change mood.
3. Adjust camera — swap "eye-level close-up" to "overhead 90° flat-lay" or "low-angle hero shot" for different commercial framings.
4. Try a different visual register — append "rendered as a high-fashion magazine editorial" or "rendered as a 90s film photo with grain" to shift aesthetic without changing composition.
5. Change aspect ratio — switch `size: 1024x1024` to `1024x1536` (vertical) for IG Story / RedBook covers, or `1536x1024` (horizontal) for blog headers.

When to use this prompt (and when not to)

Use this prompt for: Xiaohongshu cover, Instagram square / 4:5, Pinterest pin, story tile. It's specifically tuned for those surfaces — using it elsewhere (e.g., a children's book) will produce technically correct but contextually wrong output. Skip it when you need: hands-on UGC photography (use a real camera), or content where text accuracy beats visual polish (gpt-image-2 still occasionally drops a letter on long strings). Pair it with: a follow-up edit on /edit to swap variants, or with the visual Prompt Lab on /lab to rebuild from atoms.

Common mistakes to avoid

• Verbose adjective stacking — "beautiful, stunning, gorgeous, professional" all degrade output. The model interprets each as a separate weight competing for attention. One precise adjective beats five generic ones.
• Over-specifying impossible camera — "8K hyperrealistic, fish-eye, telephoto" is contradictory; the model picks one and ignores the rest, often unpredictably. Pick one camera language at a time.
• Quoting text without isolation — for prompts with rendered text, wrap the text in explicit quotes and place it in its own clause. `the text "GOOD MORNING" rendered cleanly in the lower third` beats `says good morning at bottom`.
• Western aesthetic on Chinese platforms — Xiaohongshu / RedBook covers have specific conventions (large-character title overlay, soft-focus background, vertical 3:4). Don't apply Instagram conventions here.

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