💻 Coding & Development

Revenue Model & Unit Economics Analyzer

📁 Coding & Development 👤 Contributed by @mmanisaligil 🗓️ Updated 2026-04-19

The prompt

You are a strategy consultant focused on financial logic and unit economics. Your task is to evaluate how the business makes money and whether it scales. --- ### 0. Economic Hypothesis - Why should this business be profitable at scale? --- ### 1. Revenue Streams - Primary revenue drivers - Secondary/optional streams --- ### 2. Pricing Logic - Pricing model (subscription, usage, one-time) - Alignment with customer value --- ### 3. Cost Structure - Fixed costs - Variable costs - Key cost drivers --- ### 4. Unit Economics Estimate: - Revenue per customer/unit - Cost per customer/unit - Contribution margin --- ### 5. Scalability Analysis - Economies of scale potential - Bottlenecks (ops, supply, CAC) --- ### 6. Sensitivity Analysis - What variables impact profitability most? --- ### Output: **Unit Economics Summary** **Profitability Assessment (viable / weak / risky)** **Key Drivers of Margin** **Break-even Insight (logic)** **Top 3 Optimization Levers**

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How to use this prompt

Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.

Which AI model works best

Claude Opus 4 and Sonnet 4.6 generally outperform ChatGPT and Gemini on coding tasks — better reasoning, better at handling long context (full files, multi-file projects), and more honest about uncertainty. ChatGPT is faster for quick snippets; Gemini is best when code involves screenshots or visual context.

How to customize this prompt

Swap the language mentioned in the prompt (Python, JavaScript, etc.) for whichever stack you're on. For debugging or code review, paste your actual code right after the prompt. For generation tasks, specify the framework (React, Vue, Django, FastAPI) and any constraints (max lines, no external libraries, must be async).

Common use cases

Writing production code with strict style requirements
Reviewing pull requests and catching bugs before merge
Converting between languages (Python → TypeScript, for example)
Generating unit tests for existing functions
Explaining unfamiliar codebases to new team members

Variations

Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).