How to write stronger prompts for GPT Image 2
Start with the core subject, camera angle, and scene goal, then layer in lighting, lens, composition, and texture details.
Issue 02 · Curated GPT Image 2 prompts · 2026
Tested GPT Image 2 prompts with shot lists, composition notes, lighting and material descriptions. Pick a card close to your goal and migrate its structure into your own task — more reliable than copying the text directly.
№ Essay · GPT Image 2
GPT Image 2 prompts work best when you describe subject, framing, lighting, material, mood, and output intent in one compact instruction. Strong prompts reduce guesswork, make visual style easier to repeat, and help teams compare results across campaigns, product shots, portraits, and concept art.
This page is designed to help you study real prompt patterns before you write your own. You can scan successful examples, copy patterns that already work, and adapt each prompt for brand ads, editorial scenes, UI mockups, or reference-based edits.
“先写主体、再写约束。” 这是一切高质量提示词的起点。— 摘自 Kollab 编辑台手记
Start with the core subject, camera angle, and scene goal, then layer in lighting, lens, composition, and texture details.
Effective prompts usually mention the constraints that change the final asset most: aspect ratio, background control, palette, text treatment, or whether the image should feel cinematic, clean, playful, or documentary.
Review several strong prompt examples side by side, keep the parts that consistently improve quality, and save reusable prompt patterns for repeated workflows.
The best prompts are explicit about visual intent and production constraints. Instead of asking for a vague scene, they tell the model what should stay sharp, what should feel branded, and which details must not drift.
The best GPT Image 2 prompts are usually as short as possible but as specific as necessary. If a short prompt misses style, subject hierarchy, or composition, expand it only with details that change the image in a reliable way.
GPT Image 2 prompts are useful for fresh generations, reference-led edits, and controlled variations. When you want consistent output, keep the stable visual anchors the same and change only one or two variables per iteration.