AI Image Prompting Guide

How to write effective prompts for AI image generation models (Gemini/Nano Banana, Flux, Ideogram, DALL-E, Midjourney).

Prompt Structure

A strong image prompt follows this formula:

B0

Example Prompts by Use Case

Blog hero — SaaS product:

B1

Social media graphic — announcement:

B2

Product lifestyle shot:

B3

Profile banner — professional:

B4

Directory listing — Product Hunt:

B5

Style Keywords

Photorealistic

• "commercial photography"
• "shot on Canon EOS R5"
• "editorial style"
• "natural lighting"
• "shallow depth of field"

Clean/Corporate

• "clean modern aesthetic"
• "minimal design"
• "professional corporate style"
• "bright and airy"
• "white background"

Illustrative

• "flat vector illustration"
• "isometric 3D render"
• "hand-drawn sketch style"
• "watercolor illustration"
• "line art"

Abstract/Brand

• "flowing gradient"
• "geometric pattern"
• "abstract data visualization"
• "particle effects"
• "holographic iridescent"

Tech/SaaS

• "dark mode UI aesthetic"
• "neon accent lighting"
• "glassmorphism"
• "futuristic minimal"
• "developer-focused"

Lighting Keywords

Composition Keywords

Model-Specific Tips

Gemini Image (Google)

• Best all-around for marketing images — good quality, reasonable cost
• Supports image editing — upload an existing image and describe changes
• Decent text rendering — can handle short headlines
• Specify "high resolution" for best output
• Works well with detailed, descriptive prompts
• Same API as text generation — easy to integrate

Flux (Black Forest Labs)

• Multi-image reference is the killer feature — upload product screenshots, brand assets, or style references
• Best for brand consistency across a set of images
• Use Flux Pro for final assets, Flux Dev for rapid iteration
• Flux Klein for high-volume batch generation (cheapest)
• Style transfer via reference images > style keywords in prompt
• Prompts can be shorter than other models — the references do heavy lifting

Ideogram

• Best text rendering of any model (industry-leading accuracy)
• Use when you need headlines, taglines, or brand names in the image
• Style reference system (up to 3 images) for brand consistency
• Supports "Magic Prompt" auto-enhancement
• Keep text requests simple — 3-5 words max for reliability
• Best for social graphics and banners that need text baked in

GPT Image (OpenAI)

• Current models: gpt-image-2.5-sunburst and gpt-image-2.5-flare (the gpt-image-1 family retires in late 2026; DALL-E 3 is deprecated)
• Integrated with ChatGPT — conversational image generation
• Good at following detailed prompts
• Decent text rendering (behind Ideogram, comparable to Gemini)
• Automatic prompt rewriting — may deviate from exact request
• Best for quick one-offs through ChatGPT interface
• API gives more control than ChatGPT interface

Midjourney

• Highest aesthetic quality for artistic/editorial images
• No official API — Discord-based or web interface
• Not agent-friendly — use for manual creative exploration only
• Style flags: --style raw for less stylized, --ar 16:9 for aspect ratio
• Best for hero images where pure visual quality matters most
• V6+ has improved text rendering but still unreliable

Common Prompt Mistakes

Batch Generation Workflow

When you need multiple images with consistent style (e.g., a blog series or social campaign):

1. Generate 3-4 test images with different style prompts
2. Pick the winning style based on brand fit
3. Save the exact prompt as your template
4. Use Flux multi-reference — upload the winning image as a style reference
5. Batch generate variations with the same style, different subjects
6. Post-process — add text overlays, logos, crop to platform sizes

Aspect Ratios Quick Reference

Cost Optimization

• Iterate at low quality first — use Flux Dev or Gemini Flash for drafts, upgrade for finals
• Use references over long prompts — Flux multi-reference produces more consistent results with fewer retries
• Batch similar requests — generate all blog headers in one session with the same style
• Cache and reuse — abstract backgrounds, patterns, and textures can be reused across multiple images
• Post-process instead of re-generate — crop, overlay text, and adjust color in code rather than generating new images