How Gemini code assist revolutionized my DevOps workflow in VSCode

As a DevOps engineer, my days are filled with managing complex infrastructure as code, automating deployments, and troubleshooting issues across multiple cloud environments. Efficiency and precision are critical, but so is the ability to think critically about every line of code I write. That’s why integrating Gemini Code Assist (both Standard and Enterprise editions) into my Visual Studio Code setup has been a total game-changer.

More than just an AI autocomplete

Gemini isn’t your average code completion tool. It’s an AI-powered collaborator that challenges my code decisions and encourages me to explore alternatives I might have missed. This interaction goes beyond simply suggesting code snippets — it fosters a dynamic conversation where I question and refine my solutions.

For example, when writing Terraform modules or Kubernetes manifests, Gemini helps me catch potential misconfigurations early, recommend best practices, and even suggest optimizations I hadn’t considered. This proactive approach helps reduce bugs and security risks before they reach production.

A virtual pair programmer always on call

One of the biggest benefits is how Gemini acts like a pair programmer who never tires or gets distracted. In fast-paced DevOps environments, I often have to juggle multiple tasks at once — and having Gemini constantly review my code and propose improvements feels like having a senior engineer by my side, 24/7.

This “second set of eyes” helps me:

• Identify inefficiencies in shell scripts or CI/CD pipeline configurations.
• Debate the pros and cons of different deployment strategies
• Improve infrastructure as code readability and maintainability

Boosting confidence and collaboration

What’s remarkable is how this AI collaboration increases my confidence in the code I push. I know that Gemini has questioned my assumptions and pushed me to write cleaner, more resilient code. It has essentially become a silent partner in my workflow, making me a better engineer.

Plus, when I share my code with teammates, the improved quality and clarity help accelerate reviews and reduce back-and-forth discussions. Gemini effectively elevates our team’s productivity by raising the baseline for what “good code” means.

Practical Examples: How Gemini helps with terraform

1. Catching common mistakes in resource definitions

When defining AWS resources in Terraform, it’s easy to overlook required parameters or misconfigure dependencies. Gemini instantly points out missing arguments or suggests more idiomatic ways to declare resources.

resource "aws_instance" "web_server" {
  ami           = "ami-0abcdef1234567890"
  instance_type = "t2.micro"
  # Gemini suggests adding 'tags' for better resource management
}

Gemini might prompt:

“Consider adding tags for cost allocation and easier identification, like environment = ‘dev’.”

2. Optimizing module usage

Instead of repeating resource blocks, Gemini recommends creating reusable modules or leveraging community modules.

module "vpc" {
  source = "terraform-aws-modules/vpc/aws"
  version = "3.0.0"
  # Gemini helps fill required variables and validates version compatibility
}

It guides you to maintain DRY (Don’t Repeat Yourself) principles and alerts if module versions have known vulnerabilities.

3. Enhancing security and compliance

Gemini highlights potential security risks, such as open security groups or overly permissive IAM policies.

resource "aws_security_group" "allow_all" {
  name        = "allow_all"
  description = "Allow all inbound traffic"
  ingress {
    from_port   = 0
    to_port     = 0
    protocol    = "-1"
    cidr_blocks = ["0.0.0.0/0"]
  }
  # Gemini warns: "This security group allows unrestricted access. Consider restricting to necessary IP ranges."
}

By catching these issues early, you reduce your attack surface and improve compliance posture.

4. Debating infrastructure design choices

When you’re unsure whether to use multiple small instances or fewer large ones, Gemini helps weigh trade-offs by providing pros and cons based on your code comments or context.

# Gemini comments: "Using multiple t3.micro instances can improve fault tolerance but increase management overhead."

This kind of feedback helps you make more informed architecture decisions.

Final thoughts

For anyone working in DevOps, or any code-intensive role where precision matters, tools like Gemini Code Assist are not just productivity boosters — they are trusted allies that help you think deeper, code smarter, and deploy safer.

If you haven’t tried Gemini integrated into your VSCode workflow yet, I highly recommend giving it a shot. It might just revolutionize your day-to-day work like it did mine.

How Gemini code assist revolutionized my DevOps workflow in VSCode was originally published in Google Developer Experts on Medium, where people are continuing the conversation by highlighting and responding to this story.