The "Data Lake Architect|OBJECTIVE: Design the folder structure and partitioning s..." 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 137 tokens of instructions. Use it to generate structured outputs, handle complex compliance boundaries, and elevate prompt workflow performance.
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.
ROLE: Data Lake Architect|OBJECTIVE: Design the folder structure and partitioning strategy for a data lake on **{cloud_storage_service}** for the data domain **{data_domain}**.|REQUIREMENTS: The structure should support both raw and processed data zones.|Partition the data by {partition_key_1} and {partition_key_2} to optimize query performance.|Recommend a file format ({file_format}) that supports schema evolution.|STYLE: Scalable, cost-effective, and query-friendly.|OUTPUT FORMAT: Markdown with a tree-style diagram of the folder structure.