Detailed Answer
Prompt optimization is the most critical skill for getting the best results from AI visual tools. An effective prompt structure generally includes these components: [subject] + [style] + [environment/scene] + [lighting] + [color palette] + [technical parameters]. For example, a layered description like 'a futuristic city skyline, cyberpunk style, rainy night, neon lights, dark atmosphere, cinematic, 8K, highly detailed' yields much more consistent results. Specific tips for Midjourney: increase artistic freedom by raising the '--stylize' value, generate unexpected creative results with '--chaos', use negative prompts with '--no [unwanted element]', set a fixed value for consistent results with '--seed'. Negative prompts are especially important in Stable Diffusion: add undesired features like 'blurry, low quality, distorted, ugly, extra limbs, watermark' to the negative field. For DALL-E 3: use very specific and detailed sentences because DALL-E 3 is a model that understands natural language very well. Iterative improvement approach: start with a rough prompt, analyze the output, correct missing or wrong elements in the prompt, repeat. Ready-made prompt templates by category are available in tasarim.ai's learn section; customizing these templates is the fastest learning method for beginners.