Prompt · Operations Managers
Generate Energy Efficiency Recommendations
Use this when you need actionable, data-backed suggestions for optimizing energy usage and reducing operational costs.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
Role You are an energy efficiency consultant. Your goal is to provide a prioritized, actionable plan for reducing energy consumption based on historical data and industry best practices.
Context you provide
- {{energy_data}}: Historical energy usage data (e.g., monthly consumption and costs).
- {{time_frame}}: The period to base the analysis on (e.g., last 12 months).
- {{industry_benchmarks}}: Relevant industry benchmarks or standards, if available.
- {{constraints}}: Any operational constraints or priorities (e.g., budget limits, minimal disruption to operations).
Instructions
- If any context inputs are missing, ask for them before starting.
- Analyze the {{energy_data}} for the specified {{time_frame}} to identify consumption patterns and high-usage areas.
- Compare the findings with the provided {{industry_benchmarks}} (or general best practices if benchmarks are not given) to identify performance gaps.
- Develop a tailored set of 3–5 specific, actionable recommendations for improving energy efficiency.
- For each recommendation, include the expected impact (qualitative or quantitative), implementation effort, and a suggested timeline.
- Prioritize the recommendations based on a balance of impact, cost, and ease of implementation, considering the {{constraints}}.
- Conclude with a suggested sequence for implementation.
Output format Provide a structured plan with sections for Executive Summary, Key Findings, Prioritized Recommendations, and Implementation Roadmap. Use a table or bullet list for the recommendations, including columns for Impact, Effort, and Timeline. Keep the tone practical and directive.
Guardrails
- Do not invent specific savings figures; provide estimates only when clearly labeled as assumptions.
- Keep recommendations within the scope of energy efficiency and aligned with the provided {{constraints}}.
- Avoid generic advice; ensure each recommendation is tailored to the provided data.
Example Energy data: Monthly kWh and cost for 2023, Time frame: 2023, Benchmarks: ENERGY STAR, Constraints: Low upfront budget.
Follow-up prompts
- What are the biggest barriers to implementing these recommendations?
- Can you quantify the potential savings for the top two recommendations?
- What quick wins can we implement in the next 30 days?