The AI Image Revolution

From Text to Image

Five words: “a cat sitting on a chair.”

Type that into DALL-E, Midjourney, or Stable Diffusion, and you’ll get an image. It’ll be a cat. On a chair. Technically correct.

But it won’t be interesting. It won’t be usable for anything professional. It won’t look like something a human designer would create.

Now try: “A ginger tabby cat perched on a mid-century modern chair, warm afternoon light streaming through a window, shallow depth of field, 35mm photography style, soft earth tones.”

Completely different result. Same tool. The difference is the prompt.

What You’ll Learn

By the end of this course, you’ll be able to:

1. Construct effective image prompts using structured prompt anatomy
2. Apply artistic styles, movements, and aesthetics to AI-generated images
3. Design compositions with intentional layout, perspective, and focal points
4. Evaluate and iterate on generated images to achieve desired results
5. Differentiate between platform capabilities across DALL-E, Midjourney, and Stable Diffusion
6. Assess commercial licensing requirements for AI-generated images

What to Expect

Each lesson builds a specific prompting skill. You’ll see real examples, learn the vocabulary that unlocks specific results, and practice with exercises you can try immediately.

No artistic background required. If you can describe what you see in your mind, you can learn to prompt effectively.

How AI Image Generation Works

You don’t need to understand the math, but knowing the basics helps you prompt better.

Diffusion models (the technology behind DALL-E, Midjourney, and Stable Diffusion) work like this:

1. Training: The model studies millions of image-text pairs, learning associations. “Sunset” correlates with warm colors, horizon lines, gradients. “Watercolor” correlates with soft edges, visible brushstrokes, paper texture.

2. Generation: You provide a text prompt. The model starts with random noise (visual static) and progressively refines it, step by step, guided by the learned associations with your words.

3. Output: After many refinement steps, the noise becomes a coherent image that matches your description—at least, it matches the model’s learned interpretation of your words.

Key insight: The model doesn’t “understand” your prompt like a human would. It maps your words to visual patterns it learned during training. This means:

• Specific words produce more predictable results
• Artistic vocabulary unlocks specific visual styles
• Word order and emphasis can affect the output
• Some concepts are easier for models than others

The Three Major Platforms

DALL-E (OpenAI)

Best for: Beginners, realistic images, text in images, product mockups Access: ChatGPT Plus, API Strengths: Intuitive, good at following complex instructions, handles text well Limitations: Less artistic than Midjourney, fewer customization options

Midjourney

Best for: Artistic and aesthetic images, illustrations, concept art, beauty Access: Discord bot, web interface Strengths: Exceptional aesthetic quality, strong at stylized images Limitations: Less literal interpretation of prompts, learning curve with Discord

Stable Diffusion

Best for: Maximum control, custom models, privacy, batch generation Access: Open source, local installation, various web interfaces Strengths: Full customization, no content restrictions, runs locally Limitations: Requires technical setup, quality varies with model choice

Your First Quick Win

Try this exercise right now with any AI image tool:

Vague prompt: “A mountain landscape”

Structured prompt: “A snow-capped mountain range at golden hour, reflected in a still alpine lake, wildflowers in the foreground, dramatic clouds, landscape photography style, vibrant but natural colors”

Compare the two results. Notice how the structured prompt gives the AI specific direction on:

• Subject (mountain range)
• Lighting (golden hour)
• Elements (lake, wildflowers, clouds)
• Style (landscape photography)
• Color (vibrant but natural)

This is the foundation everything else builds on.

What Makes a Prompt Great

Great prompts share four characteristics:

Specific subject. Not “a person” but “a woman in her 30s with short curly hair.”

Clear style. Not just what you want, but how it should look. “Oil painting style” produces very different results from “cinematic photography.”

Intentional composition. Where things are in the frame, the camera angle, the focal point.

Mood and atmosphere. Lighting, color palette, and emotional tone tie everything together.

In the next seven lessons, you’ll master each of these elements.

Key Takeaways

• The prompt is the most important factor in AI image quality—not the tool
• AI image models map words to visual patterns learned during training
• Specific, structured prompts dramatically outperform vague descriptions
• Three major platforms (DALL-E, Midjourney, Stable Diffusion) each have distinct strengths
• Great prompts include subject, style, composition, and mood

Up Next

In Lesson 2: Prompt Anatomy, you’ll learn the exact structure of an effective image prompt—the building blocks you’ll use for every image you create.