What is Prompt Engineering?
Every AI model has implicit biases from its training data and responds differently to the same concept phrased different ways. Prompt engineering is the skill of discovering what phrasings, structures, and modifiers produce the output you actually want.
For image generation, this means understanding how to specify subject, style, lighting, lens, and composition — often by borrowing vocabulary from photography, cinema, or art history. For LLMs, it means giving clear instructions, relevant examples, and explicit constraints.
How it works
Structure
For image prompts: subject → action → style → lighting → lens → composition. Example: 'A golden retriever puppy [subject] chasing a butterfly [action] in a meadow [scene] at golden hour [lighting] shot on 85mm f/1.4 [lens] shallow depth of field [composition].'
Negative prompts
In image generation, negative prompts specify what NOT to include. Example: 'blurry, low quality, extra fingers, watermark' removes common artifacts.
Weighted emphasis
Many tools support weighted terms — (subject:1.3) emphasizes that term, (subject:0.7) de-emphasizes it. Essential for controlling how strongly each element appears.
Common use cases
- Writing precise image prompts that capture a specific style or subject
- Crafting LLM system prompts for chatbots, agents, and coding assistants
- Building prompt templates for consistent brand output
- Chain-of-thought prompting for complex reasoning tasks
