Prompt Engineering Guide for Better AI Images

Good prompts are not long prompts. They are structured prompts.

Prompt structure that scales

A reliable template:

1. Subject: who or what is the focus
2. Context: where it exists
3. Style: visual language and medium
4. Camera: framing and lens intent
5. Lighting: mood and key light behavior
6. Constraints: things to avoid

When prompts fail, one of these blocks is usually ambiguous.

Use specific nouns, not stacked adjectives

Weak: "beautiful, stunning, amazing fantasy scene"

Stronger: "ancient basalt temple carved into sea cliffs, storm clouds, bioluminescent mist"

Concrete nouns reduce model guesswork.

Control negatives deliberately

Negative constraints are most useful when they are narrow:

• avoid extra limbs
• avoid text overlays
• avoid watermark artifacts

Avoid long negative lists unless there is a repeated failure pattern.

Iterate with a changelog mindset

Track each generation attempt with one intentional delta:

• v1 baseline
• v2 changed lens from 35mm to 85mm
• v3 changed lighting from hard noon sun to overcast diffuse

This makes quality improvements reproducible.

Build prompts with tags in Phosphene

Phosphene helps by turning prompt design into composable parts:

• define core subject tags
• layer stylistic tags
• add relation tags to resolve scene logic

The result is faster convergence and fewer "start over" moments.

Final quality check

Before exporting, verify:

• composition reads at thumbnail size
• focal subject is obvious in under one second
• style is consistent across foreground and background
• no artifact immediately breaks immersion

If one fails, revise the specific block instead of rewriting the full prompt.