Prompt Engineering for Images

A guide to crafting effective prompts for AI image generation, with examples across photography, illustration, concept art, and design.

Prompting strategies for AI-generated images

Introduction

Text-to-image models such as DALL-E, Stable Diffusion, and Midjourney have changed how visuals are created. With a few words, you can generate concept art, product mockups, or stylized illustrations. But results depend heavily on prompt engineering.

This guide covers strategies and multiple real examples for crafting effective image prompts, from photography to concept art.


Why prompt engineering matters for images

Images are highly sensitive to descriptive choices. A single adjective can change composition, mood, or realism. Careful prompting allows you to:

  • Shape style (photorealistic, cartoon, oil painting)
  • Control composition (camera angle, lighting, framing)
  • Ensure consistency across multiple generations
  • Exclude unwanted artifacts with negative prompts

Anatomy of an image prompt

A practical structure:

Subject -> Modifiers -> Style -> Details -> Negative prompts

  • Subject: main focus (example: “a medieval castle”)
  • Modifiers: adjectives (example: “weathered, ivy-covered”)
  • Style: artistic or photographic (example: “oil painting, impressionist”)
  • Details: technical specifics (example: “wide-angle, golden hour”)
  • Negative prompts: exclusions (example: “no text, no watermark”)

Use cases and examples

Photorealistic photography

  • “Portrait of a golden retriever puppy sitting in a field of daisies, photorealistic, soft sunlight, shallow depth of field, 50mm lens.” Outcome: natural-looking portrait with bokeh background.

  • “A mountain valley at sunrise, wide-angle, glowing sky, National Geographic photo style.” Outcome: dramatic, high-detail landscape shot.

  • “Aerial drone shot of a futuristic city at night, neon lights, clear sky.” Outcome: cinematic bird’s-eye view with neon glow.


Stylized illustration

  • “Flat vector illustration of a laptop on a wooden desk, pastel colors, clean design.” Outcome: minimal vector-style drawing, suitable for infographics.

  • “Fantasy forest scene, watercolor painting, soft warm tones, brushstroke texture.” Outcome: painterly look with watercolor feel.

  • “Cartoon robot cooking breakfast, bold outlines, playful, bright colors.” Outcome: cartoon illustration with humor and charm.


Concept art

  • “Cyberpunk street at night, neon signs, rainy reflections, cinematic wide shot.” Outcome: concept art for sci-fi city environment.

  • “Alien desert landscape, twin suns on the horizon, surreal colors, highly detailed.” Outcome: otherworldly concept art scene.

  • “Steampunk-inspired airship flying over a Victorian city, dramatic lighting, detailed textures.” Outcome: elaborate steampunk concept art.


UI and product design

  • “Mobile app dashboard mockup, clean layout, modern typography, light theme, vector style.” Outcome: simple wireframe-like UI draft.

  • “Minimalist landing page design, hero section with headline and call-to-action button.” Outcome: web layout draft suitable for inspiration.

  • “Set of flat vector icons for weather: sun, cloud, rain, storm.” Outcome: consistent icon set for UI or presentation.


Iterative prompting

Start simple and refine:

  1. “A forest at sunrise” -> generic scene
  2. “A misty forest at sunrise, golden light, birds in the distance” -> more atmospheric
  3. “Misty forest at sunrise, golden light, cinematic photography style, 35mm lens” -> highly stylized, photorealistic result

Negative prompting

Negative prompts help exclude unwanted artifacts:

  • “A medieval castle, oil painting style, no text, no watermark.”
  • “Photo of a sports car, cinematic lighting, no distortion, no extra wheels.”
  • “City street at dusk, cinematic, no people, empty roads.”

Outcome: cleaner generations with fewer artifacts.


Best practices checklist

  • Start broad, then refine with detail
  • Use style tags (painting, photo, vector)
  • Control composition with camera terms (close-up, aerial, wide shot)
  • Use negative prompts to exclude text, watermarks, distortions
  • Iterate: small adjustments per generation improve results

Pitfalls to avoid

  • Overstuffed prompts: too many descriptors confuse the model
  • Ambiguous terms: “modern”, “abstract”, or “futuristic” may be interpreted differently
  • Unrealistic expectations: some subjects or styles may not combine well
  • Bias: outputs can reflect stereotypes present in training data

Further reading