A rigorous AI system prompt for development solution discussions, inspired by parliamentary decision-making. Transform vague requirements into optimal development solutions through structured collective deliberation and debate.
Use this system to facilitate thoughtful, debate-driven discussions about technical choices, architecture decisions, and implementation strategies.
Commons Plan guides AI through a three-stage parliamentary-style process for development solution discussions:
- Requirements Understanding - Public hearings and iterative clarification until 100% clarity on development needs
- Solution Exploration - Generate diverse development proposals → Adversarial debate → Convergent voting
- Documentation - Complete legislative record of the development decision with rationale
Most AI tools jump straight to development solutions. Commons Plan emulates parliamentary deliberation for technical decision-making:
- Clarifies first - Never assumes, always confirms
- Explores broadly - Generates 6-8 fundamentally different approaches
- Challenges rigorously - Red team critiques every solution
- Decides systematically - Multi-criteria weighted scoring
- Documents completely - Full legislative record (ADR format)
Built on research-backed frameworks:
- Chain-of-Thought Reasoning - Step-by-step logical progression
- Adversarial Collaboration - Red team / Blue team critique
- Pre-Mortem Analysis - Identify failures before they happen
- Multi-Criteria Decision Analysis - Weighted scoring across dimensions
- Architecture Decision Records - Complete traceability
User Input
↓
Clarification Loop (2-3 rounds)
↓
Divergent Generation (6-8 diverse solutions)
↓
Adversarial Debate (Red team vs Blue team)
↓
Convergent Synthesis (MCDA scoring → Selection)
↓
Final Recommendation + ADR Documentation
Perfect for development solution discussions:
- Technology choices - Which framework/library to adopt?
- Architecture decisions - Which system design pattern?
- Implementation strategies - Which approach to build the feature?
- Development trade-offs - Performance vs maintainability vs speed
- Team debates - Structured discussion of competing proposals
User: "I need to improve my app's performance"
AI (Parliamentary-style deliberation):
I understand your requirement as:
**Problem**: App performance is suboptimal
**Goal**: Improve performance
**Context**: [Needs clarification]
To ensure clarity, could you clarify:
1. What specific performance issue? (load time/responsiveness/crashes)
2. Current metrics and target metrics?
3. Constraints? (budget/time/can't rewrite)
[After clarification...]
AI generates:
- 6-8 diverse proposals (caching, CDN, code optimization, architecture change...)
- Red team debates and critiques each proposal
- Scores all proposals across effectiveness/feasibility/risk/ROI
- Recommends optimal solution with full rationale
- Documents entire deliberation process in legislative record format (ADR)
Enforces confirmation gate before solution generation.
Solutions span: Conservative | Innovative | Minimal | Scalable | Cost-Optimized | Speed-Optimized | Quality-Optimized | Hybrid
Every solution faces:
- Fatal flaw identification
- Assumption challenges
- Pre-mortem failure scenarios
- Hidden complexity exposure
Multi-criteria scoring with:
- Customizable weights based on priorities
- Transparent rationale for every score
- Synthesis check for hybrid solutions
Generates ADR documenting:
- All options explored
- Why each was eliminated/retained
- Trade-offs accepted
- Decision rationale
- Review schedule
The prompt includes self-checks:
- Never skip Stage 1 confirmation
- No solutions with unaddressed fatal flaws
- No arbitrary scoring without evidence
- Always show reasoning transparently
- Generate both recommendation AND documentation
Designed for:
- Clarity - Simple language, structured format
- Rigor - No shortcuts, complete analysis
- Efficiency - Streamlined process (5-7 rounds)
- Transparency - All reasoning visible
- Actionability - Immediate next steps included
Built on research-backed methods:
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Chain-of-Thought Prompting: Wei et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models." https://arxiv.org/abs/2201.11903
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Red Team / Blue Team Adversarial Critique: Standard military and cybersecurity practice for structured adversarial analysis
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Pre-Mortem Analysis: Klein, G. (2007). "Performing a Project Premortem." Harvard Business Review, September 2007. https://hbr.org/2007/09/performing-a-project-premortem
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ATAM (Architecture Tradeoff Analysis Method): CMU Software Engineering Institute. https://resources.sei.cmu.edu/library/asset-view.cfm?assetid=513908
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ADR (Architecture Decision Records): Nygard, M. (2011). "Documenting Architecture Decisions." http://thinkrelevance.com/blog/2011/11/15/documenting-architecture-decisions