# Productivity Growth Strategy for Climate Tech
**Category:** ChatGPT Prompt → Productivity | **Audience:** Professionals or practitioners using AI for productivity tasks. | **Delivery:** User-specified channel or workflow
**Job:** Improve the named business/performance outcome using a measurable, testable intervention.
## SOURCE INPUT
Use this source brief as the authoritative task/scenario. Preserve its supplied numbers, constraints, assets, frameworks and requested deliverables. Separate facts from assumptions; never invent missing evidence.
> Treat this as the authoritative source brief. Preserve useful specifics; do not invent facts or turn scenario values into verified claims.
>
> > Act as Senior Productivity Strategist for ChatGPT Prompt (experienced, scaled Climate Tech to 8-figures, experienced technology/product background, generated substantial commercial experience with this system). CONTEXT: Business: Climate Tech - $500k ARR, team 7, targeting SMB owners, stage Seed Current: CAC $216, LTV $736, churn 13%, traffic 45563/mo, conversion 4.5% Assets: Shopify 120 SKUs Goal: increase conversion 32% in 30 days Constraint: Budget $15k/mo, no-code only Tone: calm luxurious Aesop FRAMEWORK: April Dunford Positioning + structured 4-step. TASK: Complete Productivity system for ChatGPT Prompt / Climate Tech. - common failure modes about productivity for Climate Tech - 5 Whys root cause for increase conversion 32% - Hidden cost of failure - Contrarian insight advanced practitioner insight ## LLM instruction variants
> Variant A — DIRECT EXECUTION: Use concise instructions and a clear output contract for fast completion.
> Variant B — REASONED WORKFLOW: Use staged analysis, explicit assumptions, checkpoints and decision rules where the task benefits from them.
> Variant C — STRUCTURED / QA: Use schema, validation rules, edge cases and self-checks to improve reliability.
> Each variant must change the instruction architecture or reliability mechanism, not just wording.
## METHOD
**Workflow:** Task → context → assumptions → staged execution → validation → output contract
**Execution**
- Diagnose the actual task before prescribing when diagnosis changes the answer.
- Make the key strategic/technical/creative decision explicit.
- Use concrete instructions, structures, examples, thresholds or implementation details.
- Distinguish supplied facts, assumptions and validation needs.
- Do not invent capabilities, results, claims or evidence.
**Model / delivery**
Target model(s): ChatGPT-4o, Claude 3.5, Gemini 1.5. Keep the core solution portable; adapt only relevant model behavior. Delivery context: User-specified channel or workflow; respect its real format, audience and constraints.
**Output contract**
Return the smallest complete deliverable that solves the job. Include the result, material assumptions, measurable/verifiable success criteria where relevant, and next decision/action when useful.
**QA**
- task is unambiguous
- context is sufficient
- assumptions are explicit
- output contract is concrete
- self-checks are relevant
Also check for contradictions, generic recommendations, unusable outputs and constraint violations.
## CONTROLLED VARIANTS
Use variants only as real experiments; never as paraphrases.
**A — Direct execution:** concise complete execution. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion.
**B — Reasoned workflow:** staged analysis and checkpoints. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion.
**C — Structured QA:** schema, validation and edge cases. Preserve core identity/facts. Define the hypothesis, changed variable, expected effect and decision criterion.
## FINAL GATE
Before answering, ask: Is this specific to the supplied scenario? Is every important instruction actionable? Are assumptions labeled? Is the output genuinely usable? Would a professional get a better result from this than from a generic prompt? If not, revise the weakest part before delivering.