If outputs don't match expectations, first refine your prompt with more specific descriptors for subject, style, lighting, and composition. Vague prompts like "nice picture" yield unpredictable results, while detailed prompts like "portrait of elderly man, warm studio lighting, shallow depth of field, Rembrandt style" guide the model effectively. Next, adjust CFG weight: increase to 8-12 for stricter prompt adherence, or lower to 3-4 for more creative interpretation. If images appear overly random or noisy, reduce temperature below 1.0. Generate multiple images in parallel to compare variations and identify optimal settings. For persistent quality issues, test the same prompt on alternative models like
Kling Image v3 to determine if the issue is prompt-related or model-specific.