How AI Image Generation Works
The basic idea
Diffusion models (used by Midjourney, DALL-E, Stable Diffusion) start from random noise and gradually refine it into a coherent image, guided by your text prompt at every step. The model has learned, from millions of image-caption pairs, what visual patterns match which words.
Why prompt wording matters so much
Unlike text models, image models respond very literally to word choice, order, and specificity. "A cat" and "a fluffy orange tabby cat sitting on a windowsill at golden hour" produce dramatically different results.
What these tools are good and bad at
Strong at: style, mood, composition, abstract concepts. Weak at: exact text rendering, precise counting (fingers, objects), and fine factual accuracy. Know these limits before you rely on AI images for anything precision-critical.
Key Takeaways
- AI image models refine random noise into images guided by your prompt.
- Word choice, order, and specificity dramatically affect the result.
- These tools excel at style and mood, but struggle with text and precise counts.
- Understanding these limits helps you use AI images appropriately.
Compare vague vs specific prompts
Generate two images from the same tool: one from a 3-word prompt, one from a detailed 25-word prompt describing subject, style, lighting, and mood. Compare the results.