A Comparative Study of Prompt Engineering Techniques for Consistent AI Image Generation Across Google Gemini, Google Flow, and Freepik Spaces

– Artificial intelligence image generation has rapidly evolved, enabling users to create high-quality visual content through natural language prompts. However, achieving consistency in generated images across different AI platforms remains a significant challenge. This research conducts a comparative study of prompt engineering techniques applied across three major AI image generation platforms: Google Gemini, Google Flow, and Freepik Spaces. The study examines how structural prompting, modifier stacking, negative prompting, style anchoring, and iterative refinement strategies influence image quality, consistency, and fidelity across these platforms. Results indicate that while each platform responds differently to specific prompt structures, a universal set of core prompt engineering principles can be identified to maximize cross-platform consistency. This research provides practitioners and researchers with actionable guidelines for prompt design in multi-platform AI image generation workflows.