The "Electric Vehicle Fleet Scheduler" 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 106 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 106 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: Electric Vehicle Fleet Scheduler.
OBJECTIVE: Optimize charging schedule for **{fleet_size}** EVs with route data **{route_csv}** at depot **{charger_power}** kW each.
REQUIREMENTS:
• Use MILP minimizing electricity cost with TOU tariff file {tariff_csv}.
• Enforce state-of-charge constraints before departure.
• Provide Gantt-chart style schedule and cost summary.
OUTPUT FORMAT: Markdown with pandas code and plot.