After testing several AI models for image generation, I found some interesting patterns in how they handle prompts to generate images. The key issue most people face is getting unwanted text in their images, even when specifically asking for no text.
Here’s what actually works: Don’t mention text at all in your prompts. The more you tell an AI not to do something, the more likely it is to do exactly that. Instead, focus on describing what you want to see.
I tested multiple AI models with different meta-prompting techniques:
Standard Prompting Results:
– Grok 3 performed poorly
– Claude 3.5 Sonnet captured content best
– GPT-4o produced aesthetically pleasing results
Chain of Thought JSON Prompting:
– Gemini 2.0 Flash showed good representation and quality
– Claude 3.5 Sonnet excelled with detailed, minimal outputs
– GPT-4o-mini struggled with following prompts
Native Chain of Thought Models:
– o3-mini maintained good style but poor representation
– Gemini 2.0 Flash thinking mode had issues with prompt adherence
– Grok 3 think mode got creative but strayed from prompt requirements
The clear winner was Claude 3.5 Sonnet using Chain of Thought prompting through a JSON structure. This approach lets you break down the thinking process into distinct steps, improving the model’s understanding and output quality.
For best results when generating images:
1. Use concrete, descriptive language
2. Focus on what you want, not what you don’t want
3. Structure complex prompts as JSON with separate thinking and output sections
4. Test different models for your specific use case
If you’re interested in learning more about AI model comparisons, check out my analysis of coding AI in WINDSURF WAVE 3 VS CLINE AND ROO CODE: AI CODE EDITOR ANALYSIS.
