GPT Image 2 Tutorial: Prompt Guide 1

GPT Image 2 is OpenAI’s latest image generation model, and it brings a significant upgrade in text rendering accuracy, visual quality, and semantic understanding. For e-commerce sellers and marketers running multilingual campaigns, one of the most practical features is multilingual image translation — taking an English poster and turning it into a Japanese, Spanish, or Arabic version with accurate text and natural typography.

In this guide, we’ll walk through how to write effective prompts for GPT Image 2 to produce high-quality, localized marketing visuals.

What Makes GPT Image 2 Different for Multilingual Content

Previous AI image tools often struggled with non-English text. You’d get garbled characters, wrong glyphs, or text that looked like an alien language. GPT Image 2 changes this in a few key ways:

• 95%+ text accuracy for Latin, CJK, Arabic, and Cyrillic scripts
• Automatic layout adaptation — left-to-right for English/Spanish, right-to-left for Arabic, vertical options for Japanese
• Cultural visual cues — the model picks up on design conventions common to the target language’s region
• Fast turnaround — most translations complete in under 60 seconds

I ran a test last week: took a simple English discount flyer and asked GPT Image 2 to recreate it in Japanese. The result came back in about 30 seconds. The Japanese text was grammatically correct, the typography felt natural, and the overall layout adapted from a horizontal English style to something that felt right for a Japanese audience.

The Prompt Framework That Actually Works

After testing dozens of prompts across multiple languages, here’s a framework that consistently delivers usable results:

Element	What to Include	Example
Subject	The core visual	”A coffee shop promotional poster”
Target language	Specific locale	”in Japanese”
Text content	Exact copy to display	”Spring Limited Offer: Second Cup Half Price”
Visual style	Mood and aesthetics	”Clean minimalist design, warm earth tones”
Layout notes	Text placement	”Title centered, price highlighted in large font”
Format	Output shape	”Portrait orientation, 4:5 ratio”

The more specific you are about the text content, the better. GPT Image 2 handles explicit instructions like “display the text exactly as: …” better than vague descriptions.

Real Example: Translating a Discount Poster

Let’s walk through a concrete example. Say you have an English promotional banner for an electronics sale, and you want a Spanish version for your LATAM store.

Original English concept:

• Headline: “Summer Tech Sale”
• Subhead: “Up to 40% off select items”
• Visual: Gadgets on a clean white background with orange accents

GPT Image 2 prompt:

Create a promotional poster in Spanish. Display this exact text: “Oferta de Verano Tech” as the main headline, and “Hasta 40% de descuento en productos seleccionados” as the subheading. Style: modern e-commerce look, clean white background, orange accent buttons, product photos of headphones and smartwatches. Layout: headline top-center, discount badge prominent in lower right. Portrait 4:5.

The output preserved the clean e-commerce feel while switching to Spanish typography conventions — slightly more decorative headline font, proper use of inverted punctuation (¿ ¡), and spacing that felt natural for Spanish readers.

I ran the same test for Arabic, Russian, and Portuguese. All came back with accurate text and culturally appropriate layouts. Arabic correctly used right-to-left text flow. Russian maintained good Cyrillic character clarity even at smaller sizes.

Common Use Cases Beyond Simple Translation

Once you get the hang of the basic prompt structure, you can extend this to more complex scenarios:

Product lifestyle shots with local text Generate a product photo with packaging labels, price tags, or promotional stickers in the target language. The model handles perspective distortion on text surprisingly well.

Social media templates Create a base template and generate 10 language variants for your global campaigns. Keep the visual structure consistent while localizing all text elements.

Localized infographics Charts, process diagrams, and instructional graphics with labels in the target language. GPT Image 2 maintains logical visual hierarchy even when text direction changes.

Pitfalls to Watch Out For

Even with GPT Image 2’s strong text capabilities, there are a few things that can trip you up:

Currency and pricing symbols The model sometimes keeps the original currency symbol ($) when generating for non-US markets. Be explicit: “display prices in euros with the € symbol” or “use yen with ¥.”

Cultural details A poster with Christmas imagery won’t resonate in markets where that’s not the primary holiday season. Mention seasonal context if it matters.

Text length expansion Some languages take more characters to say the same thing (German and Finnish are notorious for this). If your layout is tight, specify “allow extra space for longer text” or test a few variants.

Font choices While GPT Image 2 handles typography well, it occasionally picks fonts that feel slightly off for the target market. You can guide this with style descriptors like “use a modern sans-serif typical of Japanese commercial design” or “classic serif for French luxury aesthetic.”

Getting the Most Out of Iteration

Rarely does the first generation hit exactly what you need. The most efficient workflow I’ve found:

1. Generate 3–4 variants with slightly different style keywords
2. Pick the closest match and note what works
3. Refine with a follow-up prompt — “same layout but make the headline font bolder” or “keep everything but switch the language to Korean”
4. Export and minor touch-up — even 95% accuracy might need a tiny tweak in a design tool

This iterative approach usually gets you to a production-ready asset in under 5 minutes, compared to the hours it used to take to brief a designer and wait for revisions.

Final Thoughts

GPT Image 2’s multilingual capabilities aren’t just a nice-to-have feature — for anyone running global e-commerce or content campaigns, they’re a genuine productivity multiplier. The ability to generate localized visuals without hiring translators and designers for every market lowers the barrier to entry significantly.

The key is learning how to write prompts that give the model enough context about language, content, and visual style. Start with the framework above, experiment with your own products and markets, and iterate based on what works.

Ready to try it yourself?