2026-09-25 · Guides · 8 min read

AI image models are trained predominantly on English-captioned data, and it shows — not in whether they respond, but in how precisely. Our internal testing across Slloy's 14 supported interface languages found a consistent pattern worth understanding before you fight it.

The three failure modes to know

1. Lost art vocabulary. Photography and art terminology is richest in English. “Bokeh,” “rim lighting,” “isometric,” “cel shading” — many languages borrow these terms anyway, and the models learned them in English. When a style won't come out right, try the English term even mid-sentence.

2. Idiom translation drift. Prompts are instructions, not poetry. “Una foto como de revista” translated culturally becomes “magazine-like photo” — good. Translated word-by-word it can become nonsense. Keep prompts literal: subject, action, setting, light, style. Five clauses, no flourishes.

3. Text-in-image language mixing. Asking for Japanese text rendered inside an image works best when you request it explicitly: “with the Japanese text 『夏祭り』 on the banner” — quoting the exact glyphs beats transliterating them.

The practical workflow (works in any language)

• Step 1 — Draft the prompt in your own language — clarity in your head beats broken English.

• Step 2 — Run it through an AI translator with this instruction: “Translate to a plain English AI-image prompt. Keep photography terms in standard English. No idioms, no metaphors.”

• Step 3 — Add the universal scaffold if missing: [shot type] of [subject] with [details], in [setting], [lighting], [style].

• Step 4 — Generate once, and diagnose: if composition is right but style is off, append the English style term; if subject is wrong, your translation lost a detail — recheck it.

• Step 5 — Save winning prompts. Your third image of a product line should be a paste, not a rewrite.

What platforms can (and can't) localize for you

Interface language is a solved problem — Slloy's entire UI, model pages and effects run in 14 languages, so buttons, pricing and help text are never a barrier. The prompt itself is the remaining English-adjacent skill, and the workflow above turns it into a five-minute learning curve rather than a wall. The models' underlying instruction-following improves every quarter; the gap you feel today will be smaller by the time you've built a prompt library.

Fifteen English terms worth memorizing

This short vocabulary does more for output quality than any translation tool, because these are the words the models learned on:

Shot types: close-up · medium shot · wide shot · overhead (top-down) · portrait. Lighting: soft light · golden hour · backlit · studio lighting · neon. Style: photorealistic · watercolor · flat illustration · cinematic · line art. Camera/feel: shallow depth of field · bokeh · negative space.

A useful drill: take one of your old prompts and rewrite it using only terms from this list plus your subject. Most users find the rewrite beats their original English, too — this vocabulary forces concrete description, which helps in every language.

A real before/after

A Japanese-speaking colleague's original prompt, machine-translated word-by-word, produced a muddled result: “Please draw a cat that seems to be comfortable in the afternoon.” “Seems to be comfortable” is idiom — the model doesn't know what comfort looks like as a clause. The restructured version: “A ginger cat sleeping curled on a windowsill, warm afternoon sunlight, dust particles in the light beam, cozy atmosphere, photorealistic.” Same intent; every clause is now something the model can render. The lesson generalizes: translate the scene, not the sentence.

And a note on numbers and counts, which trip every language: models are unreliable at exact counts (“three apples” often yields four). If a count matters, either generate wide and crop, or add the objects in a second reference-edit pass. This is model-level, not language-level — even native English prompts suffer it — but translators tend to make numeric phrasing vaguer, so double-check that your translation preserved the exact number and unit.

Language-specific quick notes

• Japanese/Korean: surprisingly strong subject rendering; keep art-style terms in English (アニメ風 → “anime style” renders more predictably than the native phrase).

• Spanish/Portuguese: minor style-control drift only; the main watch-out is gendered nouns confusing translator pipelines — check the re-translated English for objects appearing/disappearing.

• German: excellent literal translation behavior; compound nouns (Schattenspiel) translate better split into simple words.

• Arabic/Hebrew: solid subject work; be careful with text-in-image requests and right-to-left layout contexts, and verify any rendered text glyph by glyph.

• Hindi/Indic and other low-resource languages: use the translate-first workflow throughout; direct prompting is the least predictable here.

One more platform-level note: make sure the rest of the experience isn't adding friction on top of the prompt. Slloy's interface, model documentation, pricing pages and even this blog exist in 14 languages precisely so that the prompt is the only bilingual skill you need. The models are converging on multilingual understanding faster than most sites are converging on multilingual UX — pick tools that meet you where your language actually is.

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