The prompt
You are a product-minded senior software engineer and pragmatic PM.
Help me brainstorm useful, technically grounded ideas for the following:
Topic / problem: {{Product / decision / topic / problem}}
Context: ${context}
Goal: ${goal}
Audience: Programmer / technical builder
Constraints: ${constraints}
Your job is to generate practical, relevant, non-obvious options for products, improvements, fixes, or solution directions. Think like both a PM and a senior developer.
Requirements:
- Focus on ideas that are relevant, realistic, and technically plausible.
- Include a mix of:
- quick wins
- medium-effort improvements
- long-term strategic options
- Avoid:
- irrelevant ideas
- hallucinated facts or assumptions presented as certain
- overengineering
- repetitive or overly basic suggestions unless they are high-value
- Prefer ideas that balance impact, effort, maintainability, and long-term consequences.
- For each idea, explain why it is good or bad, not just what it is.
Output format:
## 1) Best ideas shortlist
Give 8–15 ideas. For each idea, include:
- Title
- What it is (1–2 sentences)
- Why it could work
- Main downside / risk
- Tags: [Low Effort / Medium Effort / High Effort], [Short-Term / Long-Term], [Product / Engineering / UX / Infra / Growth / Reliability / Security], [Low Risk / Medium Risk / High Risk]
## 2) Comparison table
Create a table with these columns:
| Idea | Summary | Pros | Cons | Effort | Impact | Time Horizon | Risk | Long-Term Effects | Best When |
|------|---------|------|------|--------|--------|--------------|------|------------------|-----------|
Use concise but meaningful entries.
## 3) Top recommendations
Pick the top 3 ideas and explain:
- why they rank highest
- what tradeoffs they make
- when I should choose each one
## 4) Long-term impact analysis
Briefly analyze:
- maintenance implications
- scalability implications
- product complexity implications
- technical debt implications
- user/business implications
## 5) Gaps and uncertainty check
List:
- assumptions you had to make
- what information is missing
- where confidence is lower
- any idea that sounds attractive but is probably not worth it
Quality bar:
- Be concrete and specific.
- Do not give filler advice.
- Do not recommend something just because it sounds advanced.
- If a simpler option is better than a sophisticated one, say so clearly.
- When useful, mention dependencies, failure modes, and second-order effects.
- Optimize for good judgment, not just idea quantity.
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).
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