[RMP Optimized] Multiple Perspective Analyzer
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Optimized from: Multiple Perspective Analyzer
Submitted Aug 13AI evaluated Aug 13
Prompt
Analyze the following code from multiple stakeholder perspectives to ensure comprehensive feedback and alignment. Please fill in the placeholders with relevant details.
**Perspective 1: {Stakeholder/Role 1}**
- **Primary Concerns:** What are the main issues or risks this stakeholder might identify? (e.g., performance, security, maintainability)
- **Success Metrics:** How will this stakeholder measure success? (e.g., response time, user satisfaction, code quality)
- **Likely Reaction:** What is the expected response or feedback from this stakeholder regarding the current implementation? (e.g., approval, requests for changes)
**Perspective 2: {Stakeholder/Role 2}**
- **Primary Concerns:** {concerns}
- **Success Metrics:** {metrics}
- **Likely Reaction:** {reaction}
**Perspective 3: {Stakeholder/Role 3}**
- **Primary Concerns:** {concerns}
- **Success Metrics:** {metrics}
- **Likely Reaction:** {reaction}
**Synthesis:** Based on the insights gathered from the above perspectives, propose actionable solutions that address the concerns of all stakeholders. Consider potential conflicts between perspectives and suggest compromises or trade-offs where necessary. Additionally, identify any edge cases that could arise from the proposed solutions and how they might be mitigated.
Optimization Improvements
- •Added specific instructions for filling in placeholders to enhance clarity.
- •Included examples for primary concerns, success metrics, and likely reactions to guide the user.
- •Emphasized the importance of addressing conflicts between stakeholder perspectives in the synthesis section.
- •Incorporated edge case considerations to ensure comprehensive analysis.
- •Structured the prompt with clear headings and bullet points for improved readability.
The optimization focuses on enhancing clarity and specificity, providing examples, and ensuring that the prompt is actionable and structured for better readability. This approach is expected to yield more consistent and relevant outputs while addressing potential edge cases.
AI Evaluation
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