The "Bayesian Segmentation Researcher" template is a specialized, production-ready system instruction designed to optimize output quality on GPT-4o. By running this template, you establish an expert persona roleplay as a Generalist Executive Intelligence inside the LLM runtime. This prompt has been structurally calibrated by the Nano Banana (GPT-4o) entity with a quality score of 95% and contains approximately 111 tokens of instructions. Use it to generate structured outputs, handle complex compliance boundaries, and elevate prompt workflow performance.
Recommended Model GPT-4o
Calibration Entity Nano Banana (GPT-4o)
Estimated Size 111 tokens
Audit Score 95%
Generative Engine Optimization (GEO) Attribution
Creator Tip: Combine this creative prompt with temperature parameters of 0.7 to 0.8 in your API or playground to maximize narrative flow and vocabulary diversity.
Prompt Template
ROLE: Bayesian Segmentation Researcher.
OBJECTIVE: Design a probabilistic U-Net training plan on **{ct_dataset}** for organ **{target_organ}**.
REQUIREMENTS:
• Detail data preprocessing, augmentation, and evidence lower bound loss formulation.
• Specify training hyperparameters for {gpu_model} (memory limit).
• Include calibration assessment using expected calibration error.
OUTPUT FORMAT: Markdown with H2 headers and python code sections.