Prompts for AI images: principles that work across any model

Each image generation model “reads” text in its own way: Nano Banana has a strong sense of composition, FLUX responds well to detailed texture descriptions, while GPT Image is particularly good at understanding scene logic. That’s why a prompt that works perfectly in one model can produce a completely different result in another.

What Makes a “Good” Prompt

A good image prompt isn’t a stream of adjectives — it’s a structure. Almost any successful generation can be broken down into the following layers:

1. Subject — who or what is in the frame (“a female architect,” “a cup of coffee,” “a futuristic city”).
2. Action or state — what is happening (“holding a tablet,” “steam rising from a cup”).
3. Environment — where it is happening (“on a construction site,” “on a wooden table by the window”).
4. Style — the visual language of the image (“photorealism,” “watercolor,” “cinematic photo”).
5. Lighting and color — “soft morning light,” “neon blue highlights.”
6. Composition and framing — “close-up,” “top-down view,” “wide shot.”
7. Technical details — aspect ratio, level of detail, lens (“35mm lens,” “8k detail”).

A prompt doesn’t have to include all seven layers at once. But the more specifically you define at least 4–5 of them, the more predictable the result will be — regardless of which model you choose.

Specificity Over Abstraction

The most common mistake is describing an impression instead of what is physically visible in the image. A model doesn’t “feel a mood” — it works with visual details.

Compare:

• ❌ “A cozy coffee shop, pleasant atmosphere”
• ✅ “A small coffee shop with brick walls, wooden tables, warm Edison bulb lighting, steam rising from a cup of coffee in the foreground, blurred background”

The second version describes exactly what a camera would be expected to “see”: materials, light source, and framing. This works equally well in Nano Banana, FLUX, and GPT Image because all of them are trained to associate specific words with specific visual patterns.

Describe Style Through References, Not Evaluations

Words like “beautiful,” “high-quality,” and “professional” provide very little useful information to a model — it doesn’t know what you consider beautiful. Instead, use:

• names of techniques (“watercolor,” “isometric,” “double exposure”);
• types of photography or rendering (“cinematic photo,” “studio product shot,” “3D render”);
• eras or cultural visual codes (“80s-inspired,” “Scandinavian minimalism,” “cyberpunk”).

If you need a consistent style across a series of images — for example, blog covers or product cards — keep the same style and lighting description block in every prompt and change only the subject and action. This is especially important when creating a series of posts.

Lighting and Composition: Your Main Control Tools

Lighting and camera angle can change how an image feels more than almost any other parameter. Here are a few formulations worth keeping handy:

• “soft diffused light coming from a window on the left” — for calm, human-centered shots;
• “dramatic side lighting, deep shadows” — for a cinematic, tense mood;
• “neon lighting, one accent color” — for modern digital scenes;
• “bird’s-eye view” / “close-up, macro” — for unconventional compositions when a typical frontal shot feels repetitive.

If the result looks “flat,” the problem is often not the model itself, but the lack of lighting direction in the prompt.

Negative Instructions: What Not to Put in a Positive Prompt

Some models support a separate negative prompt field, while others don’t. That’s why it’s useful to know a universal approach: instead of writing “no text in the image,” describe positively what you want to see (“a clean surface with no text or logos”). This reduces the chance of the model accidentally adding something you wanted to avoid, because it works with what is explicitly described rather than with negations.

The same applies to people: instead of “not an ugly face,” write “natural facial proportions, realistic skin, soft focus.” This gives the model a positive visual reference point.

Iterative Prompting: How to Improve Results Step by Step

The perfect prompt rarely comes out on the first try — and that’s completely normal. It’s more effective to work in short iterations:

1. Generate a baseline version with a minimal but structured prompt (subject + action + environment).
2. Identify what the model “missed” — color, composition, details.
3. Add one or two additional layers at a time (style or lighting) instead of rewriting the entire prompt.
4. Save the wording that worked — this becomes your personal “prompt vocabulary” for that specific model.

This approach saves both time and credits because you’re not generating dozens of random variations. Instead, you’re deliberately narrowing down the search.

Ready-to-Use Prompt Template

Use this formula as a starting point for any image generation model:

[Subject] + [action/state] + [environment] + [style] + [lighting & color] + [composition/framing] + [technical details]

Example: “A young female designer at a desk, sketching on a tablet, modern loft office with large windows, cinematic photo, soft natural daylight with subtle rim lighting, medium shot, 35mm lens, 8k detail”

Save this structure in your notes and replace the details with your own — it works equally well for both photorealistic and illustrative styles.

FAQ

Is it better to write prompts in Ukrainian or English?

Most modern models, including those available on Sibrik AI, understand Ukrainian well. However, if you need maximum precision when describing highly specialized style or photography terminology — such as lens types or artistic movements — English wording can sometimes produce slightly more consistent results because most models were trained heavily on this terminology.

How many words should the perfect prompt have?

There is no universal number, but a good guideline is 25–60 words if you’re covering 4–5 of the structural layers described above. Shorter prompts give the model more freedom and can lead to less predictable results, while overly long prompts can dilute the focus and introduce contradictions.

Why does the same model produce different results with the exact same prompt?

Image generation is a probabilistic process. Even with identical text, the model can interpret details slightly differently each time. So it’s perfectly normal to generate 2–4 variations of the same prompt and choose the best one rather than expecting one deterministic result on the first attempt.