Definition
Prompt Engineering is the practice of designing and optimizing text inputs (prompts) to achieve specific, desired outputs from AI models. In the context of image generation, it involves crafting descriptions that effectively communicate visual concepts to AI systems.
Why Prompt Engineering Matters
The quality of your prompt directly impacts the quality of generated images. A well-crafted prompt can:
- Produce more accurate results on the first try
- Reduce the need for multiple generation attempts
- Save credits and time
- Unlock capabilities you didn't know the model had
Key Elements of Effective Prompts
Subject
Clearly define what you want to see: "a golden retriever", "a medieval castle", "a businesswoman"
Style
Specify the artistic style: "photorealistic", "watercolor painting", "3D render", "anime style"
Composition
Describe the layout: "close-up portrait", "wide landscape shot", "bird's eye view"
Lighting
Set the mood: "golden hour lighting", "dramatic shadows", "soft diffused light"
Quality Modifiers
Add enhancement terms: "highly detailed", "8K resolution", "professional photography"
Common Techniques
- Weighted terms: Some models support (parentheses) or [brackets] to emphasize or de-emphasize elements
- Negative prompts: Specify what you don't want to see
- Style references: Reference known artists or art movements
- Technical terms: Use photography or art terminology for precise control
Examples
Basic prompt: "a cat sitting on a windowsill"
Enhanced prompt: "a fluffy orange tabby cat sitting on a rustic wooden windowsill, golden hour sunlight streaming through lace curtains, photorealistic, shallow depth of field, warm color palette, 8K quality"