Ideogram Prompt Guide: Plain Text vs. JSON for Real Results
Discover how to use plain text and JSON in the Ideogram Prompt Guide for effective results in your design projects. Master structured outputs now!
Ideogram Prompt Guide: Plain Text vs. JSON for Real Results
Use plain-text prompting when you’re exploring ideas. Switch to structured JSON the moment you need repeatable, production-ready output — logos, posters, exact text placement, anything you’ll run more than once. Four levers move the needle fastest:
- Put your exact on-image text in quotes, and keep it to a handful of words.
- Front-load your subject and key details. Ideogram’s attention drops off the further into the prompt you go.
- Keep plain-text prompts under 150 to 160 words, roughly 200 tokens.
- For layouts that need to hold their shape across runs, build in Prompt Builder instead of freehand text.
Key Takeaways
JSON prompting gives Ideogram 4.0 repeatable layouts, while plain-text prompting favors speed and exploration over precision.
| Point | Details |
|---|---|
| Choose mode by job | Use plain-text for ideation, JSON for anything needing repeatable, exact output. |
| Front-load key details | Put subject and critical details early since attention weakens later in the prompt. |
| Quote and shorten on-image text | Wrap exact words in quotes and keep them brief for legible rendering. |
| Use bounding boxes for text | Non-overlapping boxes in the 0 to 1000 coordinate system prevent garbled text collisions. |
| Build visually with Prompt Builder | Use Generate mode to scaffold, then Manual mode to fine-tune boxes and export JSON. |
| Save your prompts like code | Saimonsays’s prompt refinery and template export help you version prompts for auditable, repeatable results. |
Table of Contents
- What’s the Difference Between Plain-Text and JSON Prompting in Ideogram?
- How Do You Structure a Plain-Language Ideogram Prompt?
- What Does the Ideogram 4.0 JSON Schema Actually Look Like?
- How Does Prompt Builder Help You Write JSON Without Coding It?
- Ready-to-Use Ideogram Prompt Formulas and Examples
- How Do You Fix a Prompt That Isn’t Working?
- Why Trust This Ideogram Prompting Approach?
- A Practitioner’s Take on Production Prompting
- Turn These Prompts Into Repeatable Assets
- Frequently Asked Questions
- Sources
What’s the Difference Between Plain-Text and JSON Prompting in Ideogram?
Ideogram 4.0 runs on two prompting modes: plain-text, which gets expanded by Magic Prompt into a fuller structured caption, and direct JSON, which you write yourself for exact control. Magic Prompt takes a loose idea like “a fox reading a book by candlelight” and fills in composition, lighting, and style choices you didn’t specify. That’s genuinely useful when you’re still deciding what you want.
It’s the wrong tool the moment you need the same result twice.
Plain-text works best for:
- Ideation and mood exploration when you don’t know the final look yet
- Rapid variation across dozens of concepts
- One-off social posts where “close enough” is fine
JSON works best for:
- Logos and brand marks that need pixel-consistent placement
- Posters or flyers with exact text zones
- Any asset you’ll regenerate with small tweaks and expect the same layout
Think of it as a speed-versus-repeatability trade; for creative workflows where ideation and repeatable styles matter, tools like AI‑Powered Novel Writing Platform show practical use cases. Plain-text is faster to type and better for browsing options. JSON takes longer to write but removes the guesswork, because you’re telling the model exactly where things go instead of hoping it interprets your intent the same way twice.
How Do You Structure a Plain-Language Ideogram Prompt?
A prompt that wanders rarely renders clean. The fix is ordering your details the way Ideogram’s documented prompt structure recommends, front to back:
- Image summary — one clause naming what the image is
- Main subject — who or what anchors the frame
- Pose or action — what the subject is doing
- Secondary elements — supporting objects or characters
- Background/setting — where this happens
- Lighting — the mood-setting detail most people skip
- Framing — camera angle, shot distance, composition
- Technical enhancers — style tags, rendering quality terms
Bury your subject at word 140 and you’re gambling on whether it survives the generation.
A one-line template you can reuse: [Image type] of [subject] [action], with [secondary elements], set in [background], lit by [lighting], framed as [framing], rendered in [technical style].
Assembled example: “A poster of a lone astronaut planting a flag, surrounded by drifting dust, set on a rust-colored dune, lit by a low orange sun, framed as a wide low-angle shot, rendered in gritty matte illustration style.”
Pro Tip: Name the typography’s weight, structure, and texture (bold sans, hand-painted, cracked stencil) instead of a font name — Ideogram can’t load actual font files, so describing the attributes gets you closer to what you pictured.
What Does the Ideogram 4.0 JSON Schema Actually Look Like?
The JSON schema has three top-level fields, and skipping any of them leaves the model guessing.
| Field | What it controls |
|---|---|
high_level_description |
The overall scene concept in a sentence or two |
style_description |
Rendering style, medium, mood, color direction |
compositional_deconstruction |
Background plus a list of individual elements, each with type, description, and optional position |
Inside compositional_deconstruction, each element is typed as either obj (an object, character, or shape) or text (literal on-image copy), and each needs a description that matches its type. Positioning uses a normalized 0 to 1000 coordinate system for bounding boxes, and you should only reach for bounding boxes when placement actually needs to be locked down, not on every element by default.
Non-overlapping bounding boxes matter most for text elements. Two competing boxes force the model to resolve a conflict you didn’t ask it to resolve, and the usual result is garbled or half-rendered copy.
A minimal JSON might set only high_level_description and style_description with a loose compositional_deconstruction. A bounding-box version adds explicit coordinates for a headline block and a logo mark so both land in the same spot every time you regenerate. Color palettes inside JSON should use uppercase #RRGGBB hex codes, capped at 16 total and five per element.
How Does Prompt Builder Help You Write JSON Without Coding It?
Prompt Builder is a three-panel visual tool: scene fields on the left, a canvas in the center for drawing bounding boxes, and a live JSON preview on the right that updates as you work.
- Start in Generate mode to scaffold a rough scene from a short description.
- Switch to Manual mode to drag boxes, edit per-element text, and fine-tune positioning.
- Export or copy the JSON once the canvas matches your intended layout.
The generate-then-manual workflow is the fastest path if you’re not comfortable writing raw JSON by hand, and it still produces the exact same schema under the hood.
Pro Tip: Keep text bounding boxes from touching or overlapping, even slightly. A sliver of overlap is often enough to make the model blend two text blocks into an unreadable mess.
Ready-to-Use Ideogram Prompt Formulas and Examples
The reliable formula: Subject + “exact text in quotes” + style + layout + color + aspect ratio. Swap pieces in and out; the structure holds.
- Logo: A minimalist wolf-head emblem with “NORTHPINE” in bold condensed sans, flat two-color design, square layout, navy and gold.
- Poster: A retro travel poster of a coastal cliff at sunset with “VISIT MARBLE BAY” in serif display type, centered bottom third, warm coral palette.
- Sticker: A die-cut sticker of a grinning cartoon avocado holding a coffee cup, thick white outline, transparent background.
- Merch: A t-shirt graphic with “RUN CLUB” in distressed varsity lettering, arched layout, single-color print style.
- Typography: The word “ECHO” rendered in liquid chrome letterforms with dripping edges, dark background, dramatic rim lighting.
- Photoreal: A close-up portrait of an elderly fisherman mending a net, weathered hands in focus, harbor blurred behind him, golden hour light, shot on 85mm lens.
Keeping on-image text short and quoted isn’t a style preference. It’s the difference between legible copy and a smear of near-letters, based on Picsart’s documented prompting advice for text-heavy designs.
A ready-to-paste JSON example for a poster with locked text placement would set high_level_description to the scene concept, style_description to your visual style, and inside compositional_deconstruction, a text element for the headline with a bounding box in the upper third and an obj element for the background art spanning the full canvas.
How Do You Fix a Prompt That Isn’t Working?
Change one variable at a time, in this order: color, then style, then layout. Changing all three at once makes it impossible to tell what fixed (or broke) the result.
- Text renders garbled or cut off → shorten it, rephrase the wording, or move the prompt to JSON with a defined bounding box.
- Layout keeps drifting between regenerations → stop relying on plain-text description and either add bounding boxes or export full JSON.
- Before you regenerate, confirm: exact text is quoted, aspect ratio is set, and if you’re iterating on a JSON prompt, you’ve saved the current version before editing it.
Why Trust This Ideogram Prompting Approach?
Every schema field and workflow step here traces back to Ideogram’s own prompting documentation, not guesswork from trial and error.
- Saimonsays runs a live prompt refinery for testing structures like these in real time.
- Model-specific technique guides cover Ideogram’s quirks alongside other major image and video models.
- Socratic coaching sessions help you build a personal prompt library instead of starting from zero each time.
A Practitioner’s Take on Production Prompting
JSON wins for anything you’ll run twice. Plain-text is for browsing ideas, not for shipping assets. Version your prompts like code: save the JSON, note what changed, and you’ll stop debugging generations from memory.
Turn These Prompts Into Repeatable Assets
Writing a good JSON caption once is a skill. Rebuilding it from memory every time you need a variation is a waste of your hours. Saimonsays is the shortcut between the two: a live prompt refinery where you refine an Ideogram prompt until it’s production-ready, then export the template so you’re never starting from a blank field again.
Beyond Ideogram, Saimonsays covers model-specific techniques and quirks for the other major AI and image tools you’re likely juggling, plus Socratic coaching sessions if you want someone walking through your prompt logic with you instead of guessing alone. If your team is scaling past one-off images into a real content pipeline, a workflow diagnostic can spot where prompts are bleeding time and credits before you burn through another batch of regenerations. Head to Saimonsays and export your first template, or book a free diagnostic to see where your current prompting habits are costing you.
Frequently Asked Questions
Should I use plain-text or JSON for my first Ideogram prompt? Start with plain-text if you’re still figuring out the concept. Switch to JSON once you know the exact layout you want to reproduce.
How long can an Ideogram prompt be? Keep plain-text prompts under roughly 150 to 160 words, about 200 tokens, with your most important details stated first.
Does Ideogram support specific fonts in prompts? No. Describe the typography’s weight, structure, and texture instead of naming a font, since Ideogram can’t load font files directly.
What’s the easiest way to write JSON prompts without coding? Use Prompt Builder: scaffold a scene in Generate mode, then refine bounding boxes and text in Manual mode before exporting.
Why does my on-image text look garbled? The text is likely too long or its bounding box overlaps another element. Shorten the phrase, quote it exactly, and give it its own space in the layout.