Your AI images look generic because you are asking for a feeling instead of giving an order.
If you rely on words like cinematic or epic, you are essentially letting the AI guess what you want. I found a fantastic breakdown by a Reddit user that explains why vibes fail and specific constraints win. This guide moves away from creative writing and treats prompting like a stack of hard rules.
The Shift: Constraints Over Vibes 💡
The author argues that a great prompt is a technical stack of constraints, not a story. If a sentence doesn’t enforce a specific visual rule, delete it. You need to strip away the permission to guess that vague adjectives provide to the AI.
Defensive Prompting is Mandatory
Instead of just saying what you want, you must explicitly block what you hate. The original poster suggests using a Style Exclusions section to banish the CGI look, waxy skin, or plastic texture that plagues AI art. This forces the model to steer clear of its default, cartoony tendencies.
Speak the Camera’s Language
Vague terms like high quality mean nothing to the algorithm. This expert recommends specifying the actual hardware and physics: define the lens (e.g., 35mm, 85mm), the shot type (waist-up), and the lighting behavior (rim highlights). By defining the physics, you stop the AI from hallucinating weird lighting or impossible geometry.
The "Surgical Edit" Workflow
Getting the perfect image is rarely a one-shot deal. The creator outlines a specific process: run the template, generate variations, pick the best base, and then use a High-Fidelity Edit Prompt. This involves keeping the seed identical and changing only one variable at a time to refine the output without losing the original composition.
Try This Template 📌
Here is the core structure the Reddit user recommends for consistent results:
Identity Lock: Preserve facial features, proportions, and age exactly. No beautification.
Style Exclusions: No cartoon, anime, illustration, CGI look, or waxy skin.
Style Directive: Photoreal, high-fidelity photography. Real materials and physically plausible lighting.
Camera: [Shot type], [angle], [lens], [distance].
Lighting: Key direction, fill behavior, shadow softness. No glowing edges.
Environment: [Specific location]. Minimal clutter.
Hard Exclusions: No extra fingers, warped hands, distorted text, or watermarks.
The original post includes 10 specific scripts for use cases like LinkedIn carousels and product shots, so check the link below!
💡 FAQ & Troubleshooting
Why are my generated images inconsistent or “mid” quality?
This usually happens when prompts rely on “vibes” rather than constraints. Using vague terms like “cinematic,” “epic,” or “high quality” gives ChatGPT permission to guess details, often leading to confident but incorrect results. To fix this, remove ambiguity by using a stack of specific constraints where every line enforces a specific behavior.
How do I prevent the “AI look” (waxy skin or cartoonish textures)?
Implement “defensive prompting” by using a Style Exclusions section. Explicitly list what you do not want, such as “no cartoon,” “anime,” “CGI look,” “waxy skin,” or “plastic texture.” Combine this with positive Style Directives that request “photoreal,” “natural skin texture,” and “physically plausible lighting.”
How do I keep a character’s face consistent across multiple images?
Use an Identity Lock in your prompt. Upload a reference image as the source and explicitly instruct the AI to preserve facial features, proportions, age, and skin texture exactly. You must also include hard exclusions for “beautification,” “smoothing,” or “face reshaping” to prevent the AI from idealizing the subject.
What is the best workflow for refining an image?
Once you have a base image you like, use a “surgical edit” workflow. Keep the identity, framing, lighting, and background variables unchanged, and modify only one specific variable at a time. Do not rewrite the whole prompt or add creative fluff, as this introduces drift.
Here is the prompt template to create great images with ChatGPT. Plus 10 prompts for specific image use cases
byu/Beginning-Willow-801 in