AI Image Generation Prompts: Lessons from 10,000 Renders

After generating roughly 10,000 AI images for RAXXO Studios over the past year (product designs, content series thumbnails, brand assets, and experimental art), I've developed opinions about what works, what doesn't, and what most prompt guides get wrong.

The Prompt Structure That Works

Most prompt guides tell you to describe what you want. That's necessary but insufficient. A reliable prompt has four layers:

  1. Subject: What's in the image (character, object, scene)
  2. Style: How it should look (photorealistic, illustration, 3D render)
  3. Composition: Camera angle, framing, focal length
  4. Technical: Lighting, color palette, quality modifiers

Weak prompt: "A character wearing headphones in a neon city."

Strong prompt: "A hyper-realistic 3D rendered character wearing oversized headphones, standing on a rain-wet street corner at night. Neon signs reflecting in puddles, shot from low angle, shallow depth of field, volumetric fog, cyberpunk color palette with teal and magenta accents, studio-quality render, 8K detail."

The strong prompt gives the AI specific decisions for each layer. The weak prompt leaves most decisions to chance.

Negative Prompts Are Underrated

Telling the AI what NOT to generate is often more important than what to generate. Every tool has tendencies: extra fingers, text artifacts, cluttered backgrounds, uncanny facial expressions. A good negative prompt pre-empts these:

Negative: no visible screens or phones, no readable text or letters,
no clock faces with numbers, no fine-detail close-ups on small objects,
no deformed hands, no blurry faces, no watermarks

I add negative prompts to every generation. The specific negatives depend on the subject matter, but the principle is universal: explicitly exclude known failure modes.

Consistency Is the Real Challenge

Generating one good image is easy. Generating 20 images that look like they belong to the same brand is hard. AI tools are inherently stochastic - the same prompt produces different results each time.

Strategies that help with consistency:

  • Reference images: Tools that support image-to-image or style reference give you an anchor. Use a successful output as the reference for subsequent generations.
  • Seed locking: In tools that expose the seed parameter, finding a good seed and reusing it with prompt variations maintains visual consistency.
  • Style keywords: Develop a consistent set of style modifiers that you use across all prompts. Mine include: "hyper-realistic 3D, studio lighting, volumetric atmosphere, cinematic color grade."
  • Character sheets: For recurring characters, create a detailed character model sheet that you reference in every prompt.

What Each Tool Does Best

Midjourney: Best aesthetic quality. The images "look right" with minimal prompt engineering. Weakness: limited control over exact composition and details.

DALL-E 3: Best at following complex prompts with multiple elements. Good text rendering. Weakness: tends toward a "clean illustration" look that can feel generic.

Stable Diffusion (with ComfyUI): Most control. ControlNet, LoRAs, inpainting. Weakness: steeper learning curve, requires local GPU or cloud setup.

Adobe Firefly: Best for commercial safety (trained on licensed content). Good integration with Photoshop. Weakness: quality slightly behind Midjourney.

Freepik AI: Good for video frames and motion-ready outputs. I use it for the Lexxa content series.

The 70-20-10 Rule

About 70% of my generations are usable with minor adjustments. 20% need significant editing or regeneration. 10% are completely off. If your hit rate is worse than this, your prompts need work. If it's better, you might be playing it too safe with simple prompts.

The 10% failures are actually valuable. They sometimes produce unexpected combinations that spark new ideas. Some of our best-selling merch designs came from "failed" generations that looked wrong for the original purpose but perfect for something else.

Resolution and Post-Processing

AI-generated images need post-processing for production use. Common steps:

  • Upscaling: Generate at the tool's native resolution, then upscale with a dedicated tool (Real-ESRGAN, Topaz). Generating directly at high resolution often produces worse results than upscaling.
  • Color correction: AI outputs sometimes have inconsistent white balance or oversaturated colors. A quick levels adjustment fixes this.
  • Inpainting: Fix specific areas (hands, text, small artifacts) without regenerating the entire image.
  • Background removal: For product mockups and composites, clean background extraction is essential.

Prompting for Video (Kling, Runway, Pika)

Video generation prompts are different from image prompts. Key differences:

  • Describe motion explicitly: "walking toward camera," "turning head slowly left"
  • Keep scenes simple. Complex multi-character scenes produce artifacts
  • Avoid text, screens, and fine details. Video models struggle with these even more than image models
  • Use the last frame of one clip as the reference for the next to maintain continuity

Building a Prompt Library

Save every prompt that produces a good result. I maintain a prompt library organized by category (characters, environments, products, abstract). When I need something new, I start from a proven prompt and modify it rather than writing from scratch.

Over time, you develop a personal prompt vocabulary. Specific modifier combinations that reliably produce the look you want. This library is one of your most valuable creative assets.

See AI-generated designs across 91 products at raxxo.shop, plus the Lexxa content series at raxxo.shop/pages/watch.

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