Visual AI Prompting: Why It’s Different from Text

Introduction

Up to this point, every prompt you have written has produced text. Words went in. Words came out. The structure, the modifiers, the iteration loop – all of it operated in the same medium.

Visual AI prompting works differently. Words still go in. But what comes back is an image or a video clip. That shift changes what a good prompt looks like – and it changes the skill you need to write one.

This article explains the core difference between text prompting and visual AI prompting basics. It covers why visual prompts require visual-specific language, introduces the tools you will use in each output category, and shows you what that language looks like in practice.

By the end, you will understand why text prompting habits produce inconsistent results in visual tools – and what to do instead.

Two Output Categories: Images and Video

Visual AI prompting covers two distinct output types: text-to-image generation and text-to-video generation. Both use language as input. Both respond to the same modifier-based thinking. But they ask different things from your prompts.

A still image is a single moment. It has a subject, a composition, a lighting condition, and a mood – all captured in one frame. Your prompt needs to describe that frame.

A video is a sequence of moments. It has all of the above, plus motion, progression, and change across time. Your prompt needs to describe what happens, not just what is there.

Here is how that plays out in practice. Suppose you need two assets for a brand campaign: a still hero image and a short social media clip.

Both prompts draw from the same visual language. Only the video prompt needs to describe what moves, how the camera behaves, and how long the clip runs. That distinction is the foundation of visual AI prompting basics.

Why Text Prompting Habits Don’t Transfer

Most people write their first visual prompt the way they write a text prompt: they describe the goal or subject in general terms. The results are often inconsistent.

The reason is straightforward. In text prompting, abstract language works. Words like “professional,” “modern,” or “clear” carry consistent meaning when applied to written outputs. A model can produce a “professional tone” that most readers would recognise.

In visual prompting, those same words are far less reliable. “Professional” could mean a studio-lit white background product photo, a polished editorial portrait, or a corporate headshot with a blue gradient background. Different tools – and even the same tool across different runs – will often interpret it differently.

Compare these two prompts for the same subject:

The difference is not the length of the prompt. It is the type of information in it. The first prompt describes an intent. The second describes an image. Visual tools need visual information.

The Language of Visual Prompting: Modifiers

Specific visual language is called a prompt modifier. A modifier is a descriptive element that controls a specific visual quality in the output. Modifiers tell the tool what the image or video should look, feel, and be framed as – beyond just what it depicts.

There are five core modifier types that apply to both text-to-image and text-to-video prompts. Three additional modifier types apply to video only.

You do not need all eight modifiers in every prompt. Start with subject and style. Add lighting and mood for more visual control. Add aspect ratio when format matters. Add motion, duration, and audio only when working in video. Build specificity progressively.

The Tools: Two Categories, Four Platforms

Visual AI prompting is not tool-agnostic. The same prompt submitted to different tools often produces meaningfully different results – because each tool has different strengths, different interpretation styles, and different underlying architectures. Understanding which tool you are using – and how it tends to interpret prompts – is part of the skill.

This chapter covers four platforms that represent the current landscape for content creators and professionals: two text-to-image tools and two text-to-video tools.

Text-to-Image: GPT Image and Nano Banana 2

The two image generation tools covered in this chapter are GPT Image (accessible through ChatGPT) and Nano Banana 2 (Google’s Gemini-based image model). Both are accessible to beginners, both offer free tiers, and both produce high-quality outputs from natural language prompts. They differ in one fundamental way that affects how you write for each.

GPT Image is OpenAI’s image generation capability, accessible directly through ChatGPT. It tends to be a literal interpreter: what you describe in your prompt is closely reflected in the output. If you specify a warm-toned, mid-century office with three people near a whiteboard, GPT Image tends to produce an output that matches those specifics fairly reliably. This makes it a strong choice when accuracy to brief matters – product imagery, editorial illustration, concept visualisation. The tradeoff: underspecified prompts fill gaps with defaults that may not match your intent.

Nano Banana 2 is Google’s latest image model, built on the Gemini Flash backbone. Rather than matching your prompt word-for-word, it uses reasoning capabilities to infer physics, spatial relationships, and compositional logic from what you write. Its two strongest practical differentiators are text rendering – it can generate legible headlines, product labels, and UI mockups accurately within an image – and character consistency across a series of generated images. It is also notably fast, with most images generating in under 20 seconds.

Text-to-Video: Kling 3.0 and Veo 3.1

The two text-to-video tools covered in this chapter are Kling 3.0 (developed by Kuaishou) and Veo 3.1 (developed by Google DeepMind). Both produce high-quality video from text prompts. They differ in output character, audio capability, and the types of scenes each handles best.

Kling 3.0 is best-in-class for realistic human characters in motion – facial expressions, natural body movement, and lip-sync. It generates clips up to two minutes in length and outputs at up to 4K resolution. It is a strong choice for marketing clips, social media content, product demonstrations, and any video where realistic people in motion are the central element. A free tier is available with paid plans starting at approximately $12/month.

Veo 3.1 from Google DeepMind optimises for cinematic visual quality and environmental storytelling. Its most distinctive capability is native audio generation: it produces synchronised sound alongside the video output – ambient environment, dialogue, background music, or sound effects – based on what the prompt describes. It also integrates naturally with Nano Banana 2 for a consistent image-to-video pipeline. Pricing is per-second of video generated, positioning it at the higher cost tier.

Same Prompt, Different Results

The most important principle to internalise early is this: the same prompt tends to produce different results across different tools. This is not a flaw. It is a feature of how each tool is designed.

Here is the same prompt submitted to both image tools:

GPT Image tends to construct the scene literally – each described element positioned in the frame roughly as specified. The result is often clean and accurate, with good adherence to the prompt’s visual parameters.

Nano Banana 2 tends to infer more from the implied context. It may generate ambient light bounce, background crowd depth, and surface-level physics – steam, reflections, surface textures – that were not explicitly requested, because its reasoning layer infers that a night street food market plausibly involves those elements.

Neither approach is better by default. GPT Image gives you tighter control over the literal output. Nano Banana 2 gives you a richer interpretive result, but may include elements you did not ask for. Knowing which you are working with tells you how much to specify – and what to check for in the output.

Building Your Visual Prompting Practice

The shift from text prompting to visual prompting is not about memorising a new set of rules. It is about changing what you pay attention to when you write. In text prompting, you ask: what do I want the AI to produce? In visual prompting, you also ask: what should it look like?

Here is a simple approach to start:

1. Start with your subject. Describe what appears in the image or video.
2. Add one visual modifier at a time. Begin with style, then add lighting or mood as needed.
3. Replace abstract quality words. Swap “nice” or “professional” for specific descriptions of style, light, or composition.
4. Specify aspect ratio when format matters. Square, landscape, and portrait formats are not interchangeable.
5. For video, add motion and duration. Describe what moves, how the camera behaves, and how long the clip runs.
6. Review output against intent. If the result does not match what you had in mind, adjust the specific element that is off – not the whole prompt at once.

Article 7-2 covers prompt modifiers in full detail. Article 7-4 walks through the complete five-component text-to-image prompt structure with worked examples. Article 7-5 covers text-to-video prompting and how motion changes what you need to write.

Key Takeaways