The Best 50 ChatGPT Prompts for DevOps and Infrastructure Engineers

Are You Wasting Hours on Manual DevOps Tasks? What if you could automate most of your day-to-day DevOps processes with just a few prompts? 

We have listed 50 essential ChatGPT prompts for DevOps that can transform your workflows. These prompts are designed to save time and enhance productivity.

Top 50 ChatGPT Prompts for Optimizing DevOps and Infrastructure Workflows:

With these 50 ChatGPT prompts, you’ll find the perfect tools to tackle a wide range of DevOps tasks. 

From streamlining CI/CD pipelines to automating manual processes, these prompts can significantly enhance productivity and efficiency. 

Let’s dive deeper into specific areas where ChatGPT can provide immediate value. We’ll start with infrastructure management, which is one of the most critical aspects of any DevOps operation.

ChatGPT Prompts for DevOps: Infrastructure Management

1. Linux Server Monitoring: “Craft a script using top, df, and free to monitor CPU, memory, and disk usage on Linux servers. Include instructions for compiling these metrics into a simple report format.”
2. Network and Firewall Management on RHEL: “Detail the command sequence for restarting network interfaces, reviewing active firewall rules, and diagnosing network connectivity issues on an RHEL server, emphasizing troubleshooting steps for common problems.”
3. Terraform AWS Autoscaling Deployment: “Design a Terraform script to create an autoscaling group of web servers on AWS, incorporating load balancers and auto-healing features. Ensure the configuration supports scaling based on CPU usage thresholds.”
4. Ansible Playbook for Patch Management: “Develop an Ansible playbook that automates the update process for RedHat and Debian servers using the yum and apt modules, respectively. Outline steps for scheduling regular updates and handling potential errors during the patching process.”
5. Website Monitoring with Open Source Tools: “Outline a strategy for monitoring website response time, latency, and error rates using Smokeping, Nagios, and Grafana. Provide a step-by-step guide for setting up dashboards in Grafana to display these metrics for ongoing infrastructure monitoring.”

ChatGPT Prompts for DevOps: Continuous Integration/Continuous Delivery (CI/CD)

6. Jenkinsfile for Docker and Kubernetes: “Create a Jenkinsfile that executes parallel stages for Docker image builds, unit tests, security scans, and Kubernetes deployments, specifying resource limits.”
7. CircleCI Config for a React Application: “Draft a CircleCI config.yml for a React app, detailing workflows for build, test, and deploy phases, utilizing appropriate orbs for efficiency.”
8. GitLab CI/CD Pipeline for Python Flask App: “Outline steps for configuring a CI/CD pipeline in GitLab for a Python Flask application, using GitLab CI with detailed runner configurations.”
9. Travis CI Workflow for Node.js: “Provide a Travis CI configuration for a Node.js application, structuring stages for linting, testing, and conditional deployment to production based on test success.”
10. GitHub Actions for Java Spring Boot: “Describe a GitHub Actions workflow to automate CI/CD for a Java Spring Boot app with PostgreSQL, detailing steps for build, test, and deploy actions.”

ChatGPT Prompts for DevOps: Monitoring and Logging

11. Grafana Dashboard for Web Server Metrics: “Provide a JSON template for a Grafana dashboard visualizing web server metrics from Prometheus, including request rate and error rate.”
12. Kibana Visualizations for Nginx Logs: “Detail Kibana queries and visualizations to analyze Nginx logs, focusing on frequent IP addresses and response status codes.”
13. Fluentd Configuration for Docker Logs: “Draft a Fluentd config to aggregate and forward Docker container logs to an Elasticsearch backend, specifying filter plugins.”
14. Prometheus Alerts Configuration: “Outline Prometheus alert rules for high memory usage and node down alerts, including Slack/PagerDuty integration steps.”
15. Micrometer with Prometheus for Java Apps: “Describe steps to collect Java application metrics using Micrometer and how to visualize them in Prometheus and Grafana.”

ChatGPT Prompts for DevOps: Security and Compliance

16. Kubernetes RBAC for Workloads: “Provide a guide to implement RBAC in Kubernetes for workloads, detailing roles and permissions for a sample application.”
17. Linux Server Security Checklist: “Draft a security hardening checklist for Linux servers, focusing on SSH configurations, firewall rules, and system updates.”
18. Terraform for AWS S3 Encryption: “Write a Terraform plan to enable encryption at rest for AWS S3 buckets using KMS keys, including policy attachment.”
19. TLS Encryption with Let’s Encrypt on Nginx: “Outline steps to secure a domain with TLS using Let’s Encrypt on an Nginx server serving a Node.js app, including renewal automation.”
20. PostgreSQL Audit Logging Configuration: “Describe how to enable audit logging in PostgreSQL with log rotation, focusing on compliance and security monitoring.”

ChatGPT Prompts for DevOps: Configuration Management

21. Ansible Playbook for Apache on Ubuntu: “Craft an Ansible playbook to install and configure the Apache web server on Ubuntu systems, including virtual host setup.”
22. Puppet Manifest for Nginx Load Balancer: “Provide a Puppet manifest to configure an Nginx load balancer across nodes, using a common template for consistency.”
23. Docker Cluster Management with Ansible: “Outline steps to manage a Docker cluster configuration using Ansible, detailing container module use and template directives.”
24. Chef Recipes for MySQL Configuration: “Draft Chef recipes to manage MySQL database server configurations in a multi-server environment, ensuring secure connections.”
25. Terraform for EC2 Instances and Configuration: “Design a Terraform configuration to provision EC2 instances on AWS and automate instance configuration via remote-exec.”

ChatGPT Prompts for DevOps: Version Control

26. Git Workflow for Collaboration: “List Git commands for a typical workflow including cloning, branching, committing, pushing, and pull request creation.”
27. Undoing Changes in Git: “Provide steps to revert local uncommitted changes in Git, including file checkout and restoring a previous commit.”
28. Merge Conflict Resolution in Git: “Describe commands and strategies to resolve merge conflicts in Git, ensuring a smooth feature integration process.”
29. Gitflow Workflow Implementation: “Explain how to implement the Gitflow workflow, managing release and hotfix branches, and strategies for merging and rebasing.”
30. Migrating SVN to Git: “Outline the process for migrating an SVN repository to Git with full history, using the git-svn tool for a seamless transition.”

ChatGPT Prompts for DevOps: Automation of Manual Tasks

31. Ansible Playbook for User Account Configuration: “Compose an Ansible playbook to streamline the setup and configuration of user accounts on both Ubuntu and RHEL servers, including password and permissions management.”
32. Log Parsing with Python: “Script a Python utility to parse and summarize web server logs, highlighting key statistics like traffic volume and error rates, and automate email reporting.”
33. Terraform for AWS Infrastructure: “Design a Terraform script for provisioning AWS resources such as VPCs, subnets, and security groups, aimed at optimizing infrastructure setup and security.”
34. Database Maintenance Automation: “Detail a cron job setup for automating database backups, maintenance tasks like compaction, and cleanup operations to ensure data integrity and availability.”
35. CI Pipeline with Jenkins: “Draft a Jenkinsfile to define a CI pipeline that automates the process from code pull, build, docker image creation, to registry push, ensuring continuous integration.”

ChatGPT Prompts for DevOps: Troubleshooting and Debugging

36. Nginx 502 Error Troubleshooting: “Outline common causes for a 502 bad gateway error in Nginx and provide a step-by-step troubleshooting guide to resolve these issues efficiently.”
37. Python Script Debugging with pdb: “Explain how to use pdb to trace a Python script’s execution line-by-line, offering insights into debugging practices for identifying and resolving errors.”
38. PostgreSQL Connection Timeout Debugging: “Provide a systematic approach to diagnose connection timeout issues to a PostgreSQL database, incorporating log analysis and CLI tools for effective resolution.”
39. Identifying Node.js Memory Leaks: “Describe techniques to detect memory leaks in Node.js applications using profiling tools and heap snapshots, guiding through the process for efficient debugging.”
40. Kubernetes CrashLoopBackOff Error Resolution: “List common reasons leading to CrashLoopBackOff errors in Kubernetes and offer strategies for troubleshooting and resolving these issues to maintain pod stability.

ChatGPT Prompts for DevOps: Backup and Disaster Recovery

41. MySQL Backup to S3: “Illustrate steps to set up daily MySQL database backups using mysqldump and gzip, detailing the process to store backups in an AWS S3 bucket via a cron job.”
42. PostgreSQL Master/Slave Recovery: “Provide a recovery plan for handling master/slave PostgreSQL database failures, including steps for promoting a new master server and ensuring data consistency.”
43. AWS EBS Volume Snapshots: “Detail commands for creating consistent snapshots of EBS volumes on AWS, aimed at establishing a reliable disaster recovery strategy.”
44. VMware VMs Replication to AWS: “Explain the process to replicate on-premises VMware VMs to AWS using VMware Site Recovery Manager, focusing on steps for seamless cloud disaster recovery.”
45. Datacenter Failover to AWS Cloud: “Draft a sample disaster recovery plan for an on-premises datacenter failover to the AWS cloud, including VPC peering and NAT gateways for network resilience.”

ChatGPT Prompts for DevOps: Collaboration and Communication

46. Agile Standup Meeting Practices: “Summarize effective practices for conducting standup meetings in agile teams, emphasizing participation, action item tracking, and time management.”
47. Technical Documentation Writing Guidelines: “Offer guidelines for crafting clear and concise technical documentation, incorporating visuals and examples to cater to diverse audience understanding.”
48. Remote Team Collaboration Tips: “Provide tips for enhancing team collaboration in a remote setting using tools like Slack, Zoom, GitHub, and Google Docs, focusing on communication and project management.”
49. Architecture Decision Records (ADR) Preparation: “Outline recommendations for preparing architecture decision records in software projects, aiming at knowledge sharing and decision-making transparency.”
50. IT Team Conflict Resolution Techniques: “Describe effective conflict resolution techniques for IT teams, highlighting the importance of direct communication, fact-focus, and finding common ground for consensus building.”

What Defines Great ChatGPT prompts for Devops Tasks and Workflows?

In DevOps processes, crafting an effective ChatGPT prompt is essential for harnessing AI’s full potential in streamlining workflows and solving challenges. 

The effectiveness of a prompt lies in its ability to produce relevant and actionable outcomes shaped by its clarity, context, and goal orientation. 

Here’s what makes a great ChatGPT prompt for DevOps tasks, accompanied by concise examples for each characteristic:

• Clarity and Specificity: The prompt must be precise and unambiguous, focusing on the exact requirement. For instance, “Generate a Dockerfile for deploying a Python Flask application with Redis as the cache, optimized for minimal image size.” This type of fine-tuned prompt ensures more relevant outcomes, benefiting the team.
• Context-Awareness: Including pertinent details about the task environment or constraints ensures the AI’s responses are applicable. An example is, “Provide troubleshooting steps for a 502 Bad Gateway error in an Nginx-Apache reverse proxy setup, considering SSL termination occurs at the Nginx layer.” This prompt informs the AI of the setup and issue, leading to more accurate troubleshooting steps.
• Actionable Outcome: A great prompt explicitly seeks outputs that can be directly implemented. “List the Ansible playbook commands needed to automate the deployment of a microservices architecture on AWS EC2 instances, ensuring idempotency.” This example asks for actionable commands, clarifying that the outcome should be ready-to-use Ansible playbook commands.

Using these elements ensures that the AI’s capabilities are effectively utilized, helping team members achieve more efficient and productive outcomes.

Why Should You Use AI in Your Everyday DevOps Tasks?

Embracing artificial intelligence in DevOps processes significantly transforms how team members approach software development and operations. 

It ensures that processes are accelerated and more attuned to the dynamic needs of projects.

The key benefits of integrating AI into everyday DevOps prompts underscore the technology’s potential to revolutionize traditional workflows. 

Here’s how fine-tuned models make a substantial difference:

• Faster and More Reliable Code Development: AI enhances coding speed and accuracy, reducing manual effort and errors.
• Quicker Issue and Failure Resolutions: Utilizes predictive analytics to identify and fix potential issues before they escalate, minimizing downtime.
• Customized Template Solutions: Offers tailored templates from a DevOps prompts library that adapt to specific project needs, reducing the need for extensive manual customization.

When Should You Use Simple AI Prompting in Your DevOps Workflows?

Integrating natural language AI DevOps prompts into DevOps processes offers a strategic advantage. It helps streamline operations and enhances problem-solving capabilities. 

Here are specific instances where prompting techniques can be particularly impactful in DevOps tasks:

• Automating Routine Operations: Use AI prompts, possibly from a DevOps prompts library, to automate repetitive tasks like code generation as setting up environments, initiating builds, or deploying applications. This frees up team members to focus on more complex issues.

• Troubleshooting and Debugging: AI prompt engines can assist in diagnosing issues by parsing logs, suggesting potential fixes, or identifying anomalies in system behavior.
• Learning and Adapting to New Technologies: Implement AI shot prompting when exploring new tools, providing quick summaries and best practices. A community resource like the DevOps prompts library Reddit can be helpful for finding popular and effective prompts.

Incorporating AI prompts at these junctures optimizes workflow efficiency. It enhances the team’s capacity to tackle sophisticated challenges. This ensures a smoother and more efficient development lifecycle.

Final Thoughts

As we conclude our exploration of ChatGPT prompts for DevOps, the deliberate incorporation of artificial intelligence into development workflows can markedly boost productivity.

Effective prompting techniques, when applied through fine-tuned AI models, enable the generation of immediately useful outcomes. 

Whether for code generation, troubleshooting potential issues, or accessing new ideas from the DevOps prompts library Reddit, AI-driven tools are crucial. They help automate routine tasks and solve complex problems.