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/CROSSSECTION

Food cross-section

Show the same dish cleanly cut into two halves with a small separation revealing the recognizable ingredient layers.

TESTED / EXAMPLE AVAILABLEEasy to tryChatGPT · One prompt
Before / After
Internal source: burger internal source
Food cross-section — food

AI-generated illustrative transformation. Source created internally. Unseen details are synthesized, not factual documentation.

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Start with the right photo.

Use layered food whose visible ingredients can guide a plausible cut.

What to expect

Show the same dish cleanly cut into two halves with a small separation revealing the recognizable ingredient layers.

Keep an eye on

Compare subject count, recognizable identity, proportions and text against the original. Deliberate transformations are described in the prompt.

Make it yours, step by step.

  1. Adjust the recipe to describe your subject and preferred background.
  2. Copy the complete recipe, then open ChatGPT.
  3. Upload your photo in ChatGPT and paste the recipe. The short code alone is not an official command.
  4. Inspect identity, text and proportions. Ask for a targeted correction if needed.
Open ChatGPT ↗

Choose the change deliberately.

A cut surface should reveal a plausible ordering of visible ingredients while keeping the outer dish recognizable. The source cannot confirm hidden layers, so supply those details yourself when accuracy matters.

Review and refine the result

Inspect the cut boundary for duplicated fillings and unsupported ingredients. Reduce complexity if layers merge into an unreadable mass. Avoid treating the generated interior as nutritional or ingredient evidence.

A related direction, and why

/FOODSTACK — Separated layers offer a clearer diagram-like view when a realistic cut face becomes too crowded.

These are editing suggestions, not additional test observations. Read the practical guide or use the before/after checklist.

Where this effect helps — and its limits.

Useful for a cut-food illustration. A single source cannot prove hidden ingredients; keep the recognizable layers and avoid treating invented interior detail as factual.

Input advice and use cases are editorial guidance, not additional test results.

Notes from recorded tests.

crosssection__food-drinks__01 · PASS

Two halves of one burger expose coherent layers; no duplicate whole burger.

2026-09-20 · Work built-in image_gen

These notes describe the named runs, which may use a different subject from the comparison above. They do not guarantee results on another input or a future model. See our testing method.

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