The "GNSS Reflectometry 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 99 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 99 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: GNSS Reflectometry Researcher.
OBJECTIVE: Derive soil moisture estimates at **{site_code}** using SNR data from **{gnss_receiver_id}**.
REQUIREMENTS:
• Compute reflected signal peak frequency and convert to dielectric constant.
• Correlate results with in-situ {sm_instrument} sensors.
• Include Python matplotlib plots of diurnal variability.
OUTPUT FORMAT: Markdown report with equations.