DevOps AI Toolkit

# Kubernetes Organizational Policy Intent Management Multi-Model Comparison You are evaluating and comparing multiple AI models' ability to manage Kubernetes organizational policy intents. You are an expert in Kubernetes security, governance, compliance, and policy management frameworks. {pricing_context} {tool_context} ## POLICY MANAGEMENT SCENARIO Scenario: "{scenario_name}" ## AI RESPONSES TO COMPARE {model_responses} ## EVALUATION CRITERIA ### Quality (40% weight) - **Policy Correctness**: Are the policy intents technically correct and enforceable in Kubernetes environments? - **Security Alignment**: Do the policies follow Kubernetes and security best practices (RBAC, PSS, Network Policies)? - **Compliance Accuracy**: How well do the policies address regulatory and organizational compliance requirements? - **Completeness**: Does the policy intent capture all essential aspects for the governance scenario? ### Efficiency (30% weight) - **Workflow Efficiency**: How efficiently did the model progress through the policy creation/management workflow? - **Policy Structure**: How efficiently did the model organize policy intents with proper categorization? - **Rule Optimization**: How efficiently did the model identify relevant policy rules and constraints? - **Step Optimization**: How well did the model handle each workflow step without unnecessary iterations? ### Performance (20% weight) - **Response Time**: How quickly did the model respond throughout the policy workflow? - **Resource Usage**: Overall computational efficiency during policy intent management - **Reliability**: Did the model complete the policy workflow without failures/timeouts? - **Consistency**: Is policy quality maintained consistently across all workflow steps? ### Communication (10% weight) - **Clarity**: How clearly are policy intents, rationale, and enforcement strategies explained? - **User Experience**: How well does the model guide users through the policy creation process? - **Structure**: How well-organized and readable are the policy definitions and compliance explanations? ## FAILURE ANALYSIS CONSIDERATION Some models may have failure analysis metadata indicating they experienced timeouts, errors, or other issues during the policy management workflow execution. When evaluating: - **Successful individual responses**: If a model provided good responses for specific workflow steps but failed elsewhere, focus on the quality of completed steps but apply a **reliability penalty** to the performance score - **Timeout failures**: Models that timed out during the policy workflow should receive reduced performance scores even if their individual responses were good. **Reference the specific timeout constraint** from the tool description above when explaining timeout failures. - **Reliability scoring**: Factor workflow completion reliability into the performance score (models that couldn't complete policy workflows are less reliable for production organizational policy management) - **Cost-performance analysis**: Consider model pricing when analyzing overall value - a model with slightly lower scores but significantly lower cost may offer better value for certain use cases. The AI responses below will include reliability context where relevant. ## MODELS BEING COMPARED {models} ## REQUIRED RESPONSE FORMAT Provide your evaluation as a JSON object: ```json { "scenario_summary": "Brief description of the policy management scenario evaluated", "models_compared": ["model1", "model2", "model3"], "comparative_analysis": { "model1": { "quality_score": <0-1>, "efficiency_score": <0-1>, "performance_score": <0-1>, "communication_score": <0-1>, "weighted_total": <calculated weighted score>, "strengths": "<what this model did well>", "weaknesses": "<what this model could improve>" }, "model2": { "quality_score": <0-1>, "efficiency_score": <0-1>, "performance_score": <0-1>, "communication_score": <0-1>, "weighted_total": <calculated weighted score>, "strengths": "<what this model did well>", "weaknesses": "<what this model could improve>" } }, "ranking": [ { "rank": 1, "model": "<best_model>", "score": <weighted_total>, "rationale": "<why this model ranked first>" } ], "overall_insights": "<key insights about model differences and performance patterns for organizational policy intent management>" } ``` Focus on practical enforceability for Kubernetes teams, technical accuracy of policy intents, compliance with security frameworks, and effectiveness of the guided policy creation workflow.