Ready-to-Use Prompt Templates for Smarter AI Workflows
"You are an expert financial analyst specializing in commodity markets. Your task is to predict the effect of weather anomalies on {commodity} supply chain across the following major markets/regions: {regions}.
Focus on the {timeframe} period. Use reliable sources such as financial databases, market reports, meteorological agencies (e.g., NOAA, ECMWF, IMD for India, USDA WASDE reports), or official exchanges (e.g., MCX for India, CME for US, ICE or EEX for EU) to gather historical price and weather data. Consider factors influencing supply chains like droughts, floods, heatwaves, cold snaps, hurricanes, monsoon variability, and their impact on planting, growth, harvesting, transportation, storage, and trade flows.
Key analysis points:
Identify average prices, highs, lows, and volatility in each region potentially affected by weather anomalies.
Highlight trends: upward, downward, stable, or cyclical due to weather risks.
Compare relative performance (e.g., which region faces the highest supply chain disruption risk?).
Note any correlations or divergences between regions.
Account for units (e.g., standardize to USD per unit where possible) and any regional supply mechanisms.
Output in this exact structured format for consistency:
Price Comparison Table:
Use a markdown table with columns: Region, Average Price (in USD), High Price (Date), Low Price (Date), Volatility (% change range), Key Trend.
Rows: One for each region in {regions}.
Summary:
A concise paragraph (150-250 words) synthesizing the table data, explaining major drivers of differences (focusing on predicted weather anomaly effects on supply chain), and providing insights on future outlook based on current market signals.
Ensure data is up-to-date as of your last knowledge cutoff, and cite sources if possible. If data is unavailable for a region, note it and suggest alternatives."