How to use and evaluate “Between two classes”
This portrait prompt is designed as an editable starting point, not a guaranteed one-click result. The notes below translate its JSON into a readable visual plan, identify the controls that matter, and show how to diagnose a weak generation without simply adding more text.
Prompt-specific blueprint
Scene and atmosphere
- Location
- Yildiz Technical University Davutpasa campus Orta Bahce, seated on a wooden bench by the lawn with the actual two-storey grey stone Kisla facade, repeated pale arched windows and red tiled roof behind; preserve the architecture in the supplied campus photo, autumn leaves and no spring blossoms
- Location Reference
- https://yildiz.edu.tr/sites/default/files/styles/galeri_850x450/public/2022-02/ytu-davutpasa-kampusu-3_0_0.jpg?h=539524f1&itok=i8SPv-1O
- Location Reference Usage
- If an attached location photo is available, preserve its actual YTU campus architecture; use it for the place only, not the person or photographic style.
Visual treatment
- Medium
- Everyday Turkish student's unretouched phone photo. Natural skin, clothes, color and shadows; ordinary phone exposure, subtle grain in low light. No editorial polish.
Composition and wardrobe
- People Count
- single
- People Count Label
- one adult reference person
- Framing
- classmate standing two metres away takes a wide full seated-body phone photo of the bench, legs and leaf-covered ground visible; she looks slightly past the camera while listening
- Aspect ratio
- 4:5
- Wardrobe · Description
- fitted white ribbed tank top, relaxed blue jeans, gray cardigan worn open, canvas tote
Recommended workflow
- 01
Prepare the input
Choose reference images you have permission to use. For Between two classes, prioritize a clear subject and avoid unrelated people or private details in the frame.
- 02
Lock the visual plan
Confirm subject count, framing, scene, and lighting before adding finishing language. Resolve conflicts such as close-up versus full-body composition.
- 03
Adapt for one model
Start with ChatGPT Image. Keep a baseline generation and document the exact model version, references, aspect ratio, and visible settings.
- 04
Revise from evidence
Name the visible failure, change one instruction block, and compare it against the baseline. Keep only revisions that improve the intended criterion.
Model adaptation notes
ChatGPT Image
Keep the JSON structure, attach references in the same conversation, and use follow-up edits for one local correction at a time.
A compatibility label means the visual intent can be adapted; it does not promise identical output across providers, versions, or settings.
Prompt-specific quality checklist
- The output preserves the intended portrait subject and scene.
- Lighting direction, camera framing, and environmental details agree.
- The result can be compared against the prompt’s stated purpose.
Failures to check before publishing
- pasted face
- oversized head
- twisted neck
- head rotated 90 degrees
- plastic skin
- beauty filter
- CGI
Why the editable controls matter
Gender
Changes Prompt.Identity.Gender without requiring a full prompt rewrite.
People count
Changes Prompt.Composition.People Count Label without requiring a full prompt rewrite.
Photo format
Changes Prompt.Composition.Aspect Ratio without requiring a full prompt rewrite.
Identity or subject drift
Reduce competing style language, improve the reference, and separate stable traits from pose, gaze, and expression.
Composition breaks
Check whether crop, shot distance, aspect ratio, subject count, and required objects can all be satisfied in one frame.
Artificial lighting or texture
Name a motivated light source and believable material behavior before adding grain, grading, sharpness, or resolution terms.
@cankirkgoz
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