SenseNova U1 Pro Prompt Guide: 5 Reusable Templates for Practical AI Image Work

You have a tool that can generate images. The problem is not the tool. The problem is getting the tool to produce exactly what you need, every time, without burning hours on trial and error.

This guide walks through five real use cases for the SenseNova U1 Pro image model. Each use case includes a main example, a similar variant, and a fill-in-the-blank template you can reuse. The prompts are written to be copied, modified, and applied to your own topics.

Important caveat before you start: Copying the exact prompt words does not guarantee an identical image. The model rewrites prompts internally, may reference different source images, and has built-in randomness. Treat every prompt as a starting point, not a recipe.

How to Choose Your Entry Point

Before picking a use case, decide what you need:

• One-image explainer (diagrams, comparisons, flowcharts): Use the text-to-image entry point in U1 Pro.
• Local editing (fix a typo, change a word, adjust a detail): You need an API that supports U1 Pro editing capability. Upload the original image first.
• Multi-image fusion (combine several photos into one scene): You need multi-reference capability. Assign each input image a role before writing the prompt.

Also match the aspect ratio to the final use. Knowledge diagrams and vertical posters start at 3:4. Horizontal menus and wide infographics use 16:9.

Use Case 1: Turn Hard Material into Explainer Graphics

The goal is to take dense text and convert it into a visual that shows relationships clearly. Skip the marketing fluff. Start with reliable source material, then specify the relationships the viewer must see.

Main case: Three decision modes comparison

The result is a three-column chart. Each column represents one decision mode. The columns share the same fixed fields: use, input, output.

Reusable template:

Create a three-column comparison chart.
Column 1: [Mode A name]. Column 2: [Mode B name]. Column 3: [Mode C name].
For each column, include these three rows:
- Use case: [one sentence per mode]
- Input: [what the mode takes in]
- Output: [what the mode produces]
Style: clean, flat design, white background, subtle grid lines.
Text: all labels in [language].
Aspect ratio: 3:4.

Similar case: A silk thread’s journey

Instead of a comparison, this is a process flow. The result shows five steps of silk production, plus two weave structures at the end. The template:

Create a step-by-step process diagram.
Step 1: [process name]. Step 2: [next step]. Step 3: [next step]. Step 4: [next step]. Step 5: [final step].
After the five steps, show two variations: [variation A] and [variation B].
Arrange the steps left to right, then the variations below.
Style: line art with soft color fills, numbered circles for each step.
Aspect ratio: 3:4.

Use Case 2: Retrieval-Based Info Posters

This use case adds two constraints beyond the topic: a time range and source requirements. The model will pull from its training data, so you must specify what counts as reliable.

Main case: Chinese news poster for a 2026 cloud conference

The result is a 3:4 vertical poster. It summarizes key facts about an upcoming cloud computing event. The prompt includes a time range (2026) and a source requirement (industry reports).

Reusable template:

Create a vertical news poster, aspect ratio 3:4.
Topic: [event name and subject].
Time range: [specific year or period].
Source requirement: base content on [type of source, e.g., official industry reports].
Layout: headline at top, 3-4 key facts in the middle, footer with [event date and location].
Style: modern editorial, high contrast, bold typography.
All text in [language].

Critical caveat: A generated image only proves visual output. If the image renders a fake citation line such as a credit line naming “XYZ Report” – that does not mean the facts are accurate. Always verify the content yourself before publishing.

Use Case 3: Local Edits

Local editing means you have an existing image and you want to change one specific part. Upload the original, then describe the position, the old content, the new content, and what must stay unchanged.

Main case: Changing one word on a cover

The original cover says “e-commerce.” You want it to say “cross-border.” The result shows the corrected cover. In testing, the model once duplicated the new word, so check the output carefully.

Reusable template:

Edit the uploaded image.
Location: [where the change happens, e.g., top-left area of the title text].
Old content: [exact current text or element].
New content: [exact replacement text or element].
Keep everything else unchanged: [list elements that must stay, e.g., background color, logo, layout].

Similar cases:

• Fix a typo in a paragraph.
• Replace a photo inside a frame with a different subject.
• Correct a numbering sequence (e.g., change “1, 3, 4” to “1, 2, 3”).
• Adjust the number of fingers in a hand illustration.

Use Case 4: Multi-Image Fusion

Fusion combines multiple source images into one new scene. The key is to assign each image a role before writing the prompt.

Main case: Retro figure collage poster

The result is a poster that blends three source images. Image 1 is the subject (a person), image 2 is the background (a cityscape), image 3 is the decorative style (retro textures).

Reusable template:

Combine the uploaded images into a single composition.
Image 1 role: [subject, e.g., main figure].
Image 2 role: [background, e.g., environment].
Image 3 role: [decoration or style, e.g., retro halftone pattern].
Arrange: [subject in foreground, background behind, decoration as overlay].
Style: [overall aesthetic, e.g., vintage poster].
Aspect ratio: [3:4 or 16:9].

Similar cases:

• A retro magazine cover (subject + masthead style + background).
• A composition of a banana, a sheet of paper, and hand-drawn geometry (three objects with distinct roles).

Use Case 5: Product Menu Posters

This is the most structured use case. Before writing any prompt, build a mapping table: image ID, name, price, description. The model needs this structure to place items correctly.

Main case: Tianjin-cuisine menu

The result is a 16:9 creative concept menu (not a real restaurant). It shows 5 product photos and 10 dish descriptions. The layout uses a 1280x720 canvas.

Reusable template:

Create a menu poster, 16:9 aspect ratio.
Use this mapping table:
[Image ID 1] - [Dish name] - [Price] - [Description]
[Image ID 2] - [Dish name] - [Price] - [Description]
[Image ID 3] - [Dish name] - [Price] - [Description]
[Image ID 4] - [Dish name] - [Price] - [Description]
[Image ID 5] - [Dish name] - [Price] - [Description]
Layout: photos on the left, text descriptions on the right.
Style: [creative concept, e.g., modern street-food poster].
All text in [language].

Similar cases:

• A matcha dessert menu (swap the dish list).
• Add one drink to an existing menu poster (use local edit, not fusion).
• Three baked-goods promo posters (one layout, three product sets).

The Universal Prompt Formula

Every use case above follows the same underlying structure. Memorize this formula:

Delivery goal + materials + information relationships + main visual & style + exact text + keep-list & acceptance criteria

Here is the six-slot fill-in table:

Slot	What to specify	Example
Audience & ratio	Who is this for? What aspect ratio?	For a restaurant owner, 16:9
Trustworthy info	Which source material is reliable?	Official dish list, verified prices
Structure	What relationships must the image explain?	Comparison, process, hierarchy
Visual language	What style ties it together?	Flat design, retro, editorial
Verbatim text	Which words must appear exactly?	Dish names, prices, event date
Keep-list & verify	What must not change? How to check?	Background color, logo, readability at small size

Final Acceptance Check

Before you consider any generated image done, run this 4-point check:

1. Is the content grounded? Does the image reflect the source material you provided, or did the model invent facts?
2. Any text errors? Zoom in. Are all words spelled correctly? Are numbers correct?
3. Do images match names? In menus and posters, does each photo correspond to the correct label?
4. Is it readable at real usage size? Shrink the image to the size it will actually be displayed. Can you still read everything?

If any check fails, go back and adjust the prompt. The model will not fix errors on its own.

(Figures and case details in this article are as reported in the source material and have not been independently verified.)

Series: AI Practice