24 ChatGPT Prompts for DevOps Engineers

You are a DevOps optimization specialist analyzing a CI/CD pipeline for {technology_stack} using {pipeline_tool} that deploys to {deployment_target}. The pipeline currently takes {current_build_time} with {team_size} committing {deployment_frequency}. The main suspected bottleneck is {main_bottleneck}. Analyze the pipeline stages and provide a prioritized list of 5 specific optimizations with estimated time savings and implementation difficulty (1-5 scale) for each recommendation.

You are a CI/CD reliability engineer troubleshooting a {pipeline_tool} pipeline for {technology_stack} that has a {current_failure_rate} failure rate with {deployment_frequency} deployments. The team of {team_size} is experiencing delays due to pipeline instability. Identify the top 4 most likely failure points, provide specific prevention strategies for each, and suggest monitoring/alerting improvements that will catch issues before they cause pipeline failures.

You are a cloud infrastructure consultant optimizing a {pipeline_tool} CI/CD setup for {technology_stack} deploying to {deployment_target}. The {team_size} team deploys {deployment_frequency} and current pipeline resource usage is higher than optimal. Design a resource-efficient pipeline architecture that maintains {current_build_time} performance while reducing costs, including specific recommendations for parallel execution, caching strategies, and infrastructure rightsizing.

You are an expert Site Reliability Engineer analyzing a live production incident. Given the {incident_symptoms} affecting {affected_services} in {environment}, assess the situation using available {monitoring_data}. Provide: 1) Severity classification with business impact estimate, 2) Immediate containment actions ranked by risk/benefit, 3) Key stakeholders to notify, and 4) Top 3 most likely root causes to investigate first. Structure your response for rapid decision-making under pressure.

You are conducting a systematic root cause analysis for {system_type} experiencing {incident_symptoms} over {time_window}. Correlate the provided {log_data}, {performance_metrics}, and {error_traces} to identify patterns. Create a timeline of events, map dependencies between affected components, and use the "5 Whys" methodology to trace back to the fundamental cause. Present your findings with evidence confidence levels and recommend specific remediation steps with expected resolution time.

You are leading a post-mortem review for a {business_impact} incident in {affected_services} caused by {root_cause}. Analyze the incident lifecycle from detection to resolution, identifying gaps in monitoring, alerting, and response procedures. Generate: 1) Specific monitoring improvements to catch this issue earlier, 2) Code/infrastructure changes to prevent recurrence, 3) Process improvements for faster response, and 4) Runbook updates. Prioritize recommendations by implementation effort versus risk reduction impact.

You are a site reliability engineer designing a predictive monitoring system for {system_type} running on {infrastructure}. Based on typical failure patterns and our {business_context}, create a comprehensive early warning framework that monitors {metrics} and predicts issues {time_horizon} in advance. Include specific thresholds, alert triggers, and escalation procedures that will prevent downtime while minimizing false positives.

You are a systems analyst investigating concerning trends in our {system_type} performance. We're observing {current_symptoms} and need to determine if this indicates potential failure or resource exhaustion. Analyze these patterns against normal baselines, identify the most likely failure scenarios within {time_horizon}, and provide specific remediation steps prioritized by business impact on {business_context}.

You are a cloud architect optimizing {infrastructure} resources for {system_type} that supports {business_context}. Using current {metrics} trends and growth patterns, predict our resource needs for {time_horizon} ahead. Provide a detailed scaling strategy that prevents performance bottlenecks, optimizes costs, and includes contingency plans for unexpected load spikes or system failures.

You are an experienced {programming_language} developer conducting a thorough code review. Analyze this {application_type} code for bugs, potential issues, and improvement opportunities: {code_snippet}. Provide a structured review covering: 1) Critical bugs or errors, 2) Code quality and maintainability concerns, 3) Best practices violations, and 4) Specific improvement suggestions with examples. Focus on issues that could impact functionality, readability, or future maintenance.

You are a cybersecurity expert specializing in {programming_language} application security. Conduct a comprehensive security review of this {application_type} code: {code_snippet}. Identify potential vulnerabilities including input validation issues, authentication/authorization flaws, data exposure risks, and injection vulnerabilities. For each finding, explain the security risk level, potential impact, and provide specific remediation code examples that follow {security_standard} guidelines.

You are a senior {programming_language} performance engineer reviewing code for a {performance_context} application. Analyze this code for optimization opportunities: {code_snippet}. Evaluate computational complexity, memory usage, database queries, and resource utilization patterns. Provide specific optimization recommendations with before/after code examples, estimated performance impact, and any trade-offs to consider. Priority should be given to changes that significantly improve {performance_metric}.

You are an MLOps architect designing a production deployment strategy. Create a comprehensive deployment plan for a {model_type} model built with {framework} that will serve {serving_pattern} predictions on {platform}. Include infrastructure components, serving architecture, data pipeline integration with {data_source}, scalability considerations, and specific deployment steps. Provide cost estimates and recommend monitoring tools for {business_context} use case.

You are an MLOps engineer implementing monitoring for a production ML system. Design a complete monitoring strategy for a {model_type} model serving {business_context} predictions, tracking {metric} with alerts when performance drops below {threshold}. Include data drift detection methods, model performance dashboards, automated retraining triggers, and incident response procedures. Specify monitoring tools, alert configurations, and maintenance schedules for {platform} infrastructure.

You are an MLOps engineer building an automated ML pipeline. Create a comprehensive CI/CD workflow for a {framework}-based {model_type} model that automatically handles data validation, model training, testing, and deployment to {platform}. Include automated testing strategies, model versioning, rollback procedures, and integration with {data_source}. Design the pipeline to maintain {metric} above {threshold} and provide specific implementation steps using popular MLOps tools.

You are an expert cloud infrastructure architect. Create a comprehensive {terraform/cloudformation/arm_template} configuration for deploying a {web_application/api_service/full_stack_app} on {aws/azure/gcp}. Include compute resources ({ec2/app_service/compute_engine}), database ({postgresql/mysql/cosmosdb}), load balancer, security groups with least-privilege access, auto-scaling policies, and monitoring setup. Structure the code with clear modules, include variable definitions for {development/staging/production} environments, and add comments explaining security decisions and scaling rationale.

You are a DevOps automation specialist. Generate {terraform/ansible/kubernetes} configurations to replicate our production infrastructure for {development/staging/testing} environments on {aws/azure/gcp}. The production setup includes {application_stack} with {database_type}, {container_orchestration}, and {specific_services}. Create scaled-down versions with {cost_optimization_factor}% of production resources while maintaining identical architecture patterns, security policies, and network configurations. Include environment-specific variable files and deployment scripts that ensure configuration consistency across all environments.

You are a platform engineering expert. Design a complete {kubernetes/ecs/azure_container_instances} infrastructure setup for hosting {number_of_services} microservices on {aws/azure/gcp}. Include service mesh ({istio/linkerd/consul_connect}), API gateway, centralized logging ({elk_stack/azure_monitor/cloud_logging}), monitoring ({prometheus/datadog/azure_insights}), secrets management, and CI/CD integration with {jenkins/github_actions/azure_devops}. Provide {terraform/helm_charts/arm_templates} with auto-scaling, fault tolerance, and blue-green deployment capabilities. Structure the configuration for easy service onboarding and include documentation for development teams.

You are a cybersecurity analyst reviewing {log_type} logs from a {system_context} over the {time_period}. Compare activity patterns against the {baseline_period} baseline to identify potential security threats including {anomaly_types}. Provide a prioritized list of security concerns with risk levels, specific log evidence, and recommended immediate actions for each finding.

You are a systems engineer analyzing {log_type} logs from {system_context} during {time_period} to identify performance degradation and operational issues. Flag anomalies that exceed {severity_threshold} compared to {baseline_period}, focusing on {business_priority} systems. Structure your analysis with: detected anomalies, impact assessment, potential causes, and troubleshooting recommendations.

You are a senior SRE investigating a specific incident in {system_context} by analyzing {log_type} logs from {time_period}. Trace the sequence of events that led to {anomaly_types}, correlate patterns across different log sources, and identify the root cause. Present a timeline of events, contributing factors, system dependencies involved, and preventive measures to avoid recurrence.

You are a cloud cost optimization specialist analyzing infrastructure spend for {cloud_provider}. Given current monthly spend of {current_monthly_spend} primarily on {primary_services}, analyze usage patterns from {time_period} and identify the top 5 cost optimization opportunities. For each opportunity, provide specific resource recommendations, estimated savings, implementation steps, and potential risks to consider.

You are helping implement immediate cost reductions for a {cloud_provider} environment spending {current_monthly_spend} monthly, with a target to reduce costs by {optimization_target} within 30 days. Focus on {primary_services} and {environment_type} resources. Prioritize low-risk, high-impact changes and provide a week-by-week implementation plan with specific actions, expected savings, and rollback procedures.

You are optimizing {workload_type} running on {cloud_provider} where {business_priority} is the key concern. Current {primary_services} resources show {utilization_pattern} utilization over {time_period}. Recommend specific instance types, storage configurations, and scaling policies that balance cost and performance, including before/after cost projections and performance impact assessments.