How to Make Infographics With an AI Image Generator: 205 Prompts Analysed
Nine layouts cover the corpus. The prompts that work name the axis, state the count and quote every label — measured across 205 infographic prompts.
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What an infographic prompt has to do that a photo prompt does not
An infographic prompt fails differently from a portrait prompt. The image can be beautiful and still be wrong — the wrong number of steps, labels pointing at nothing, a title with a typo. We took the 205 prompts in the LocalBanana gallery that ask for an infographic, diagram, cutaway, exploded view or flowchart — 1.7% of 11,763 usable prompts as of 7 September 2026 — and measured how the ones that work handle layout, text and count.
205
1.7% of the gallery corpus, and the smallest category we have measured
186
exactly twice the site-wide median of 93
37
against 12 for the corpus as a whole — three times the engagement of the typical item
Nine layouts account for nearly everything
Infographic prompts are not free-form. Almost every one asks for a recognisable layout, and nine layouts cover the corpus. The share of prompts that ask for each:
| Layout | Share of infographic prompts |
|---|---|
| Callouts, labels or pointer arrows | 61.0% |
| Grid or panels | 42.9% |
| Components or ingredients laid out separately | 40.5% |
| Process, steps or flowchart | 22.0% |
| Diorama or miniature 3D scene | 16.6% |
| Cutaway or cross-section | 13.7% |
| Spec sheet, catalogue page or blueprint | 12.2% |
| Comparison or before-and-after | 10.7% |
| Isometric view | 8.8% |
| Exploded view | 7.8% |
| Timeline | 7.8% |
Three in five prompts ask for callouts. That is the thing that makes an image an infographic rather than an illustration — a line from a label to a part — and it is also the thing models get wrong most often, because a callout has to point at something specific. The prompts that get clean callouts share a habit: they say how many, and they say which side.
By subject, 32.7% of infographic prompts are about a product or machine, 25.4% about a scientific or educational topic, 16.6% are recipes, and 8.8% are business roadmaps or org charts. Recipes are the small category with the outsized result: their median is 155 views, four times the infographic median.
Exploded views and cutaways: name the axis, name the parts
The exploded view is the most-viewed layout in the corpus — median 130 views — and the two prompts below are the shortest and one of the most complete ways to ask for one.
Twenty-four words. It works as a template because the bracket does the work: swap in the object and the rest — exploded view, white background, internal components, studio lighting — is fixed.
product design, [object or vehicle with material accents], exploded view diagram, white background, three-dimensional, highly detailed internal components, studio lighting, product photography, best quality
The same idea with the composition decided. 'Arranged in a strict vertical composition along a central axis' is the sentence that makes an exploded view read as engineering rather than debris; '70% of the frame' and 'slight downward tilt' remove the two decisions the model would otherwise make badly.
A photorealistic premium exploded view visualization of a Scarlett drip coffee maker. The coffee machine, featuring a black plastic body with metal accents, is completely disassembled with all components floating in mid-air, arranged in a strict vertical composition along a central axis. Photorealistic 3D render, high-end appliance advertising photography level (Octane / Cinema 4D style), PBR materials, extremely high detail on plastic, glass, and metal textures. Frontal view with a slight downward tilt. Vertical aspect ratio (9:16). The object occupies approximately 70% of the frame. The mood conveys premium quality, cleanliness, engineering precision, and the concept of "seeing how coffee works."
The difference between them is not length. The short prompt leaves the arrangement to the model; the long one fixes the axis, the camera tilt and how much of the frame the object fills. When an exploded view comes back as a scatter of parts, it is almost always because no axis was named.
Cutaways follow the same rule. The successful cross-section prompts specify the cut plane (sliced cleanly down the centre, cut face facing the camera straight-on) and what the cut reveals (4 stacked floors, every internal layer). One prompt in the corpus asks for realistic debris at the cut edge and rebar in the section — a detail that makes a museum-style cutaway read as solid rather than hollow.
Processes and timelines: say the number
38.5% of infographic prompts state an explicit count — exactly 6 structures, 8 labels, 9 distinct layers — and 20.5% go further and number the items (1. Data collection, Step 1). Among process and timeline prompts specifically, 42.9% write the count down; the rest say several stages or the key steps and get anywhere from three to nine. Models do not count well from adjectives.
'Generate exactly 6 distinct structures' and then the six, named and ordered with arrows. The prompt also fixes what separates them (fissures or light strips in the floor) and what materials each era may use — so the count, the order and the visual boundary between stages are all decided before the model starts.
Generate a hyper-realistic, 3D "Timeline Diorama" visualizing the evolution of libraries. Generate exactly 6 distinct structures. Divide human history (5000 BC to 2100 AD) into 6 equal milestones: Ancient → Classical → Gothic → Industrial → Modernist → Future. For each era, identify the most iconic architectural style for libraries. The Platform: A long, continuous Museum Display Plinth (Marble or Concrete) extending diagonally into the distance. Subtle "fissures" or light strips in the floor separate the eras. Construct each building using materials strictly available in that time period: Ancient (Mud, Thatch, Rough Stone), Classical/Medieval (Marble, Wood beams, Stained Glass), Industrial (Red Brick, Iron, Soot), Modern (Concrete, Steel, Plate Glass), Future (Bioplastic, Aerogel, Holograms, Greenery). In front of each building, place a Physical 3D Date Label made of the era's dominant material. Camera: Isometric or High-Angle Diagonal. Scale: 1:100 Architectural Model style. Atmosphere: The lighting evolves across the image (Sunrise on the left → Noon → Sunset → Neon Night on the right). Ultra-Wide Aspect Ratio 21:9, Architectural Render, Museum Lighting.
A second habit from the process prompts: stating the reading direction. One recipe prompt in the corpus asks for a Z-shaped layout flow (top-left → top-right → bottom-left), and the phrase does more for legibility than any style adjective, because it tells the model which way the arrows go. The multi-panel guide covers the same problem for comics and storyboards, where the panel order is the whole point.
For before-and-after comparisons, the corpus's most-viewed pattern is a single raised base split into two environments — same camera, same scale, contrasting details — rather than two separate images side by side.
A split diorama template: one base, two halves, tiny people for scale, a title and the words 'Before & After' in a fixed position. The last sentence — text must match the background contrast — is a fix for the most common failure in dark-background infographics.
Create a clear, 45° top-down isometric miniature 3D split diorama showing [SUBJECT] before and after [TRANSFORMATION]. Use soft refined textures, realistic PBR materials, and balanced lighting across both sides. Design a single raised base divided into two environments with contrasting details. Include tiny stylized people to emphasize scale and change (no facial details). Use a clean solid [BACKGROUND COLOR] background. At the top-center, display [SUBJECT NAME] in large bold text, beneath it show 'Before & After' in medium text, and place a minimal transformation icon below. All text must automatically match the background contrast (white or black).
Text: the title is written out, the labels are quoted
This is where infographic prompts diverge most from every other category, and where the model split is widest.
| Text instruction | Share of infographic prompts | GPT Image | Nano Banana Pro |
|---|---|---|---|
| Title spelled out in the prompt | 55.1% | 70.9% | 30.3% |
| Words for hierarchy (heading, subtitle, caption, footer) | 45.4% | 63.8% | 16.7% |
Placeholder slots like [TOPIC] or [OBJECT] | 34.1% | 38.6% | 31.8% |
| Font or typeface named | 24.4% | 24.4% | 24.2% |
| Colour palette or hex codes | 23.4% | 29.9% | 12.1% |
| Steps or labels numbered | 20.5% | 25.2% | 13.6% |
Legibility demanded (readable, no typos, exact text) | 20.0% | 26.8% | 10.6% |
| Output language specified | 11.7% | 15.0% | 7.6% |
no text | 2.4% | 2.4% | 3.0% |
More than half of infographic prompts write the title out in full, usually in quotes — against a corpus in which most prompts never mention text at all. On GPT Image it is seven in ten. The legible-text demand is also concentrated there: 26.8% of GPT Image infographic prompts say some version of readable or no spelling errors, against 10.6% for Nano Banana Pro. People have learned which route they need to nag.
The single most effective text habit in the corpus is quoting the labels. Not label the ingredients but "200g spaghetti", "150g mushrooms", "3 garlic cloves". A quoted string is a fixed target; a description of a label is a request for the model to invent one, and invented labels are where the typos live.
Fifty-three words, seven of them quoted labels with quantities. Everything the model might misspell is spelled for it, and the process steps are icons rather than text — so there is nothing left to get wrong.
Create step-by-step recipe infographic for creamy garlic mushroom pasta, top-down view, minimal style on white background, ingredient photos labeled: "200g spaghetti", "150g mushrooms", "3 garlic cloves", "200ml cream", "1 tbsp olive oil", "parmesan", "parsley", dotted lines showing process steps with icons (boiling pot, sauté pan, mixing), final plated pasta shot at the bottom
Which route renders those strings most reliably is a controlled question, and the answer is in which AI image generator renders text best — the same twelve typographic prompts sent to each model, with every output shown. If your labels are in Chinese or Japanese, 11.7% of the corpus states the output language explicitly, and the prompts that do are far more likely to get consistent script.
Language declared as a top-level key ('Traditional Chinese'), the headline quoted, a 3×3 grid with a count of 9, and one title plus one descriptor per card. GPT Image, 323 words; the JSON exists to keep nine cards from drifting.
{"type":"herbal tea variety infographic poster","language":"Traditional Chinese","style":"clean premium editorial food photography collage, bright high-key lighting, soft natural shadows, white background, elegant wellness aesthetic","subject":"a 3 by 3 grid of caffeine-free herbal tea varieties presented in transparent glass teacups with matching botanicals scattered around each cup","text":{"headline":"無咖啡因花草茶品種大全"},"layout":{"canvas":"vertical poster","background":"warm off-white paper tone","grid":{"rows":3,"columns":3,"count":9,"cardStyle":"rounded rectangular photo cards with narrow gaps","imagePosition":"top image with title and subtitle beneath each card"},"sections":[{"title":"洋甘菊茶","position":"row 1 column 1","count":1,"labels":["花香柔和，口感溫潤舒緩"],"image":"clear glass cup of pale golden chamomile tea with floating white-and-yellow chamomile flowers, many loose chamomile blossoms scattered on the white surface"},{"title":"薄荷茶","position":"row 1 column 2","count":1,"labels":["清涼醒鼻，入口清新甘爽"],"image":"clear glass cup of light green mint tea filled with fresh mint sprigs, extra mint leaves placed in front on a white background"},{"title":"洛神花茶","position":"row 1 column 3","count":1,"labels":["酸甜明亮，果香濃郁迷人"],"image":"clear glass cup of vivid ruby red hibiscus tea with dried hibiscus calyxes around it and a glass pitcher pouring or placed behind"},{"title":"玫瑰花茶","position":"row 2 column 1","count":1,"labels":["花韻優雅，香氣細膩柔美"],"image":"clear glass cup of soft pink rose tea with dried rosebuds and scattered pink petals surrounding the cup"},{"title":"菊花茶","position":"row 2 column 2","count":1,"labels":["清香淡雅，茶湯甘潤順口"],"image":"clear glass cup of pale yellow chrysanthemum tea with whole dried yellow chrysanthemum flowers around the base"},{"title":"薰衣草茶","position":"row 2 column 3","count":1,"labels":["草本芬芳，尾韻柔和安定"],"image":"clear glass cup of translucent purple lavender tea with lavender sprigs and a blurred jar of lavender in the background"},{"title":"桂花茶","position":"row 3 column 1","count":1,"labels":["蜜香清甜，香氣溫暖悠長"],"image":"clear glass cup of pale golden osmanthus tea filled with tiny floating yellow osmanthus blossoms, more blossoms scattered around"},{"title":"檸檬草茶","position":"row 3 column 2","count":1,"labels":["柑橘清香，風味爽朗輕盈"],"image":"clear glass cup of pale green lemongrass tea with cut lemongrass stalks beside the cup and slender leaf pieces steeping inside"},{"title":"蝶豆花茶","position":"row 3 column 3","count":1,"labels":["色澤夢幻，口感清淡柔順"],"image":"clear glass cup of vivid sapphire blue butterfly pea flower tea on a saucer, decorated with blue petals and blossoms around the cup"}]},"typography":{"headline":"large dark brown serif Chinese type centered at top","cardTitles":"bold dark brown Chinese serif centered beneath each image","captions":"smaller gray Chinese text centered beneath each title"},"colorPalette":{"dominant":["cream white","dark brown","soft gray"],"accentTeaColors":["pale gold","fresh green","ruby red","blush pink","lavender purple","osmanthus gold","lemongrass green","butterfly pea blue"]},"constraints":["all 9 drinks shown in transparent glass cups","realistic food photography, not illustration","consistent white studio tabletop","minimalist luxurious layout","traditional Chinese text only","balanced spacing and clean margins"]}Style, background and ratio
Infographic prompts split almost evenly between two visual languages: 37.6% ask for a photoreal or 3D render and 30.7% for flat, vector or hand-drawn illustration. The two rarely mix in one prompt, and the highest-viewed items in the corpus are the photoreal ones — a rendered coffee maker with real materials, a diorama with PBR textures — rather than the flat charts that the word infographic usually suggests.
Background is stated more often than in product prompts: 26.8% ask for white, paper or parchment, 7.3% for a blueprint, 5.4% for a dark ground. The two diorama templates above both end with a clause forcing the text to contrast with whatever background colour is filled in, and that is not a coincidence — illegible titles on a mid-tone base are the commonest failure in the category.
The ratio distribution is the one that separates infographics from every other category in the gallery:
| Aspect ratio | Infographic prompts | Product prompts, for comparison |
|---|---|---|
| 1:1 | 20.5% | 18.6% |
| 2:3 | 18.5% | 12.8% |
| 16:9 | 16.6% | 8.6% |
| 9:16 | 13.2% | 17.9% |
| 3:4 | 9.8% | 23.7% |
| 3:2 | 6.8% | 2.7% |
| 4:3 | 4.4% | 1.4% |
Landscape ratios (16:9, 3:2, 4:3) are 27.8% of infographic prompts — more than double their share in product photography prompts. Timelines and exploded views want width; recipe posters and step guides want height. Decide which before writing the layout, because the aspect ratio determines whether eight steps fit in one row or two.
A 16:9 presentation board that reads left to right: 2D plans, then elevations and section, then a photoreal render. The prompt names the sequence and the transition in tone between them — which is what a landscape infographic needs that a portrait one does not.
An expert architectural illustrator's presentation board for a [STYLE] residence featuring [KEY ARCHITECTURAL ELEMENTS]. The canvas flows left to right: black and white 2D drawings (Site Plan, Floor Plans) on the left, Elevations and Cross-Section in the center, and a photorealistic 3D render at [TIME OF DAY/LIGHTING] on the right. Unified aesthetic blending [LINEWORK STYLE] with [TEXTURE/MATERIAL]. [TECHNICAL DRAWING TONES] transitioning to [RENDER COLOUR PALETTE]. Title block reads '[PROJECT NAME]'.
Length and structure: twice as long, and JSON does not win here
The median infographic prompt is 186 words — double the site-wide 93 and half again longer than product prompts at 123. A quarter run past 291 words and one in ten past 437. That is not verbosity; a prompt that names six stages, eight labels and a title has more to say.
33.7% of infographic prompts are written as JSON, three times the site-wide rate of 11.1%. And here the corpus contradicts itself: across all 9,599 prompts we measured in August, JSON prompts had 11× the median views of prose. Among infographics, prose leads — 57 median views against 17 for JSON.
The explanation is the model mix, not the format. 52.0% of GPT Image infographic prompts are JSON; 4.5% of Nano Banana Pro ones are. GPT Image items in this category have a median of 23 views and Nano Banana Pro items 290, so a comparison of JSON against prose here is mostly a comparison of one model's feed against the other's. Control for model and the effect disappears in both directions. The honest reading: for infographics, JSON is a tool for holding a grid of nine cards or eight sections stable, not a lever on engagement.
Which model people brief for infographics
| GPT Image | Nano Banana Pro | Midjourney | |
|---|---|---|---|
| Infographic prompts | 127 (62.0%) | 66 (32.2%) | 11 (5.4%) |
| Share of that model's own corpus | 3.1% | 1.9% | 0.3% |
| Median words | 233 | 117 | 60 |
| Written as JSON | 52.0% | 4.5% | 0% |
| Title written out | 70.9% | 30.3% | 18.2% |
| Grid or panel layout | 58.3% | 18.2% | 18.2% |
| Diorama or isometric scene | 22.0% | 22.7% | 9.1% |
| Cutaway or cross-section | 10.2% | 19.7% | 18.2% |
| Exploded view | 3.9% | 15.2% | 9.1% |
| Recipe or food | 10.2% | 28.8% | 18.2% |
People send GPT Image the text-heavy, grid-based infographics — the poster with a headline, eight panels and a footer — and write them long and structured. They send Nano Banana Pro the sectional ones: exploded views, cutaways, recipes staged as photography. Dioramas go to both in equal measure. Midjourney barely appears; 0.3% of its corpus is infographic work, and the prompts that exist are short.
The split matches what the two routes are good at. GPT Image holds a large amount of typography in a grid; Nano Banana Pro holds a rendered object and its materials. If the piece is mostly words in boxes, brief the first. If it is mostly an object with labels on it, brief the second, and quote the labels.
A brief you can copy
The prompts that produce clean infographics decide five things before describing any style: layout, count, reading direction, exact text, and what must stay readable. This template puts them in that order.
Type: [exploded view / cutaway / process diagram / timeline / recipe poster / spec sheet] of [OBJECT or TOPIC]
Layout: [single vertical axis / 45° isometric diorama on a raised base / 2×4 grid / left-to-right board]; reading order [top to bottom / Z-shaped, top-left to bottom-right]
Count: exactly [6] [stages / components / cards], numbered [1–6], in this order: [A → B → C → D → E → F]
Callouts: [8] labels, [4] on the left and [4] on the right, thin [black / gold] leader lines pointing at the part they name
Text, quoted: title "[EXACT TITLE]" at top centre; subtitle "[EXACT SUBTITLE]"; labels "[LABEL 1]", "[LABEL 2]", … ; language [English / Traditional Chinese]; all text must contrast with the background
Style: [photoreal 3D render, PBR materials, studio light / flat vector, two-colour, hand-drawn line]; background [pure white / warm paper / matte black]
Ratio: [16:9 for a timeline or board / 3:4 for a poster / 1:1 for a single object]
Do not: [add text beyond the quoted strings / invent extra parts / place labels over the object]
Delete the lines you do not need, but keep Count and Text, quoted — those are the two places where the corpus's failed infographics went wrong. If the subject is a real product you have a photo of, the reference workflow in AI image editing prompts, tested applies: state what must not change before the layout.
detailed infographic about how coffee is made, professional, clean design, easy to read
process diagram, exactly 5 stages left to right: harvest, drying, roasting, grinding, brewing; each stage a photoreal 3D object on a raised white plinth; numbered 1–5 in black sans-serif; title 'FROM CHERRY TO CUP' quoted at top centre; 16:9; no text beyond the title and the five stage names
FAQ
Can AI image generators make infographics?
Yes, with limits. In a 205-prompt corpus the layouts that work reliably are exploded views, cutaways, dioramas, numbered process diagrams and recipe posters — images where the information is mostly spatial and the text is a title plus a handful of labels. Dense data charts with many numbers remain unreliable, because every number is a chance for a typo.
How do I get an AI infographic with the right number of steps?
Write the number, then list the steps. Exactly 6 stages: A → B → C → D → E → F produces six; several stages produces anywhere from three to nine. 38.5% of infographic prompts in the corpus state an explicit count and 20.5% number the items — the rest leave the count to the model.
How do I stop AI infographics from having spelling mistakes?
Quote every string you want rendered — the title, the subtitle, each label — and add no text beyond the quoted strings. Prompts that describe a label (label the ingredients) ask the model to invent text; prompts that quote it ("200g spaghetti") give it a fixed target. 20% of infographic prompts explicitly demand legible text, rising to 26.8% on GPT Image.
Which AI model is best for infographics?
The corpus splits the work. GPT Image receives 62% of infographic prompts and gets the text-heavy, grid-based ones — 70.9% of them write the title out and 58.3% ask for a grid. Nano Banana Pro gets the spatial ones: exploded views (15.2% of its infographic prompts), dioramas and recipes staged as photography. Brief the first for words in boxes and the second for an object with labels on it.
Should I write infographic prompts in JSON?
Use it when you need to hold many parallel elements stable — nine cards in a grid, eight sections with their own labels. 33.7% of infographic prompts are JSON, three times the site rate, and 52% of GPT Image infographic prompts are. But JSON does not raise engagement in this category once you control for model; it is a structural tool, not a quality lever.
