Top ChatGPT Prompts for DevOps Engineers

 As a DevOps engineer, your role involves juggling complex tasks such as automation, infrastructure management, CI/CD pipelines, and troubleshooting. Leveraging AI tools like ChatGPT can significantly streamline your workflow, saving you time and effort. This article explores how DevOps engineers can effectively use ChatGPT and offers actionable prompts to enhance productivity.

Why DevOps Engineers Should Use ChatGPT

In the fast-paced DevOps world, automation is key to staying ahead. ChatGPT, powered by advanced language models, acts as a versatile assistant, helping with:

1. Code Generation and Debugging: Automate repetitive coding tasks and quickly identify bugs.
2. Documentation: Create high-quality, easy-to-understand documentation.
3. Learning and Knowledge Sharing: Simplify complex concepts or upskill in new technologies.
4. Infrastructure as Code (IaC): Generate Terraform, Ansible, or CloudFormation scripts.
5. Incident Management: Assist in root cause analysis and resolution.

10 Essential ChatGPT Prompts for DevOps Engineers

Here are some powerful prompts you can use:

1. Generate Terraform Templates

• Prompt: “Create a Terraform script to provision an AWS EC2 instance with a security group allowing SSH and HTTP access.”

2. Debug a CI/CD Pipeline

• Prompt: “My Jenkins pipeline is failing at the build stage due to a permissions issue. Suggest potential fixes.”

3. Automate Shell Scripts

• Prompt: “Write a bash script to monitor CPU and memory usage and alert if thresholds exceed 80%.”

4. Optimize Kubernetes Configuration

• Prompt: “How can I optimize a Kubernetes deployment for high availability and scalability? Provide an example YAML file.”

5. Write Documentation

• Prompt: “Generate detailed documentation for a Jenkins CI/CD pipeline that deploys a Node.js application to AWS.”

6. Explain Complex Concepts

• Prompt: “Explain the differences between Docker Swarm and Kubernetes in simple terms.”

7. Incident Management

• Prompt: “Help troubleshoot why my NGINX server is returning a 502 Bad Gateway error.”

8. Create Ansible Playbooks

• Prompt: “Write an Ansible playbook to install Apache2 on Ubuntu and start the service.”

9. Generate Learning Resources

• Prompt: “Summarize the best practices for securing a DevOps pipeline.”

10. Root Cause Analysis

• Prompt: “Suggest steps to debug a network issue where a service running on port 8080 is unreachable.”

How to Use ChatGPT Effectively

1. Be Specific: Clearly define the problem or task in your prompt.
2. Iterate: Review the AI’s response and refine your query for better results.
3. Combine Tools: Use ChatGPT alongside other DevOps tools for maximum efficiency.
4. Stay Secure: Avoid sharing sensitive information or credentials.

Final Thoughts

ChatGPT is a powerful ally for DevOps engineers, enabling faster problem-solving, better automation, and streamlined workflows. By incorporating these prompts into your daily routine, you can focus on higher-level strategic tasks while ChatGPT handles repetitive and time-consuming ones.

What are your favorite ChatGPT prompts as a DevOps engineer? Share them in the comments below!

Terraform Commands (CLI)

 HashiCorp Terraform is an open-source infrastructure as a code software tool that allows DevOps engineers to programmatically provision the physical resources an application requires to run.

Infrastructure as code is an IT practice that manages an application's underlying IT infrastructure through programming. This approach to resource allocation allows developers to logically manage, monitor, and provision resources -- as opposed to requiring that an operations team manually configure each required resource.

AWS Automation using Terraform

What is Terraform?

HashiCorp Terraform is an open-source infrastructure as code (IaC) software tool that allows DevOps engineers to programmatically provision the physical resources an application requires to run. Infrastructure as code is an IT practice that manages an application's underlying IT infrastructure through programming.

What is AWS Automation?

Automation, a capability of AWS Systems Manager, simplifies common maintenance and deployment tasks of Amazon Elastic Compute Cloud (Amazon EC2) instances and other AWS resources. ... Build automations to configure and manage instances and AWS resources.

Here is a full tutorial video (Concept + Demo) based on "How we can do AWS Automation using Terraform"👇👇

Resource: aws_launch_configuration

Provides a resource to create a new launch configuration, used for autoscaling groups.

Example Usage

data "aws_ami" "ubuntu" {
  most_recent = true

  filter {
    name   = "name"
    values = ["ubuntu/images/hvm-ssd/ubuntu-trusty-14.04-amd64-server-*"]
  }

  filter {
    name   = "virtualization-type"
    values = ["hvm"]
  }

  owners = ["099720109477"] # Canonical
}

resource "aws_launch_configuration" "as_conf" {
  name          = "web_config"
  image_id      = data.aws_ami.ubuntu.id
  instance_type = "t2.micro"
}

Using with AutoScaling Groups

Launch Configurations cannot be updated after creation with the Amazon Web Service API. In order to update a Launch Configuration, Terraform will destroy the existing resource and create a replacement. In order to effectively use a Launch Configuration resource with an AutoScaling Group resource, it's recommended to specify create_before_destroy in a lifecycle block. Either omit the Launch Configuration name attribute, or specify a partial name with name_prefix. Example:

data "aws_ami" "ubuntu" {
  most_recent = true

  filter {
    name   = "name"
    values = ["ubuntu/images/hvm-ssd/ubuntu-trusty-14.04-amd64-server-*"]
  }

  filter {
    name   = "virtualization-type"
    values = ["hvm"]
  }

  owners = ["099720109477"] # Canonical
}

resource "aws_launch_configuration" "as_conf" {
  name_prefix   = "terraform-lc-example-"
  image_id      = data.aws_ami.ubuntu.id
  instance_type = "t2.micro"

  lifecycle {
    create_before_destroy = true
  }
}

resource "aws_autoscaling_group" "bar" {
  name                 = "terraform-asg-example"
  launch_configuration = aws_launch_configuration.as_conf.name
  min_size             = 1
  max_size             = 2

  lifecycle {
    create_before_destroy = true
  }
}

With this setup Terraform generates a unique name for your Launch Configuration and can then update the AutoScaling Group without conflict before destroying the previous Launch Configuration.

Terraform - should I use user_data or remote-exec?

Terraform - should I use user_data or provisioner to bootstrap a resource?

You should use user_data. The user data field is idiomatic because it's native to AWS, whereas the remote-exec provisioner is specific to Terraform, which is just one of many ways to call the AWS API.

Also, the user-data is viewable in the AWS console, and often an important part of using Auto Scaling Groups in AWS, where you want each EC2 Instance to execute the same config code when it launches. It's not possible to do that with Terraform's remote-exec provisioner

Terraform Tutorial

Terraform User_Data

user_data

The user_data only runs at instance launch time.

Here is a sample of using user_data embedded into tf file:

resource "aws_instance" "user_data_example" {   ami           = lookup(var.ami_id, var.region)   instance_type = var.instance_type #  subnet_id     = aws_subnet.public_1.id   # Security group assign to instance   vpc_security_group_ids = [aws_security_group.allow_ssh.id]   # key name   key_name = var.key_name   user_data = <<EOF #! /bin/bash                 sudo yum update -y sudo yum install -y httpd.x86_64 sudo service httpd start sudo service httpd enable echo "<h1>Welcome to Httpd Server</h1>" | sudo tee /var/www/html/index.html EOF   tags = {     Name = "Ec2-User-data"   } }

 But we prefer to use a file() function:

resource "aws_instance" "user_data_example_input_file" {   ami           = lookup(var.ami_id, var.region)   instance_type = var.instance_type #  subnet_id     = aws_subnet.public_1.id   # Security group assign to instance   vpc_security_group_ids = [aws_security_group.allow_ssh.id]   # key name   key_name = var.key_name   user_data = "${file("apache_config.sh")}"   tags = {     Name = "Ec2-User-data-with-file"   } }

The apache_config.sh looks like this:

#! /bin/bash sudo yum update -y sudo yum install -y httpd.x86_64 sudo yum service httpd start sudo yum service httpd enable echo "<h1>Welcome to Httpd Server</h1>" | yum tee /var/www/html/index.html

Run Terraform Apply and see the Public IP in browser to see the above message