AI assistants have become an integral part of my development workflow. Here are practical ways I leverage AI to write better infrastructure code, debug faster, and learn new technologies more efficiently.
Writing Terraform Configurations
When creating new infrastructure resources, I often start by describing what I need in plain English. AI helps generate the initial Terraform blocks with proper syntax and common best practices:
- Generating resource blocks with correct argument names
- Suggesting appropriate IAM policies with least-privilege principles
- Identifying missing dependencies between resources
- Recommending variable structures for reusability
Debugging Lambda Functions
When my Lambda functions throw cryptic errors, AI excels at interpreting stack traces and suggesting fixes. A recent example: I was getting a DynamoDB validation error that turned out to be a missing type descriptor in my boto3 client code.
# The error message was confusing, but AI identified the issue:
# Wrong: Key={'visitor_count': 'main'}
# Right: Key={'visitor_count': {'S': 'main'}}Learning New Services
Instead of reading through pages of documentation, I ask targeted questions about specific use cases. This accelerates learning while still building deep understanding:
- "What's the difference between API Gateway REST and HTTP APIs?"
- "When should I use DynamoDB on-demand vs provisioned capacity?"
- "How does CloudFront Origin Access Control differ from Origin Access Identity?"
Code Review Partner
Before committing code, I often ask AI to review for potential issues. It catches things like:
- Security misconfigurations (overly permissive IAM policies)
- Missing error handling in Lambda functions
- Inefficient resource configurations that could increase costs
- Missing tags for resource organization
Writing Tests
AI is particularly helpful for generating test cases. Given a Lambda function, it can suggest edge cases I might not have considered and help structure pytest fixtures with moto for AWS mocking.
The Key: Stay Critical
While AI accelerates development, I always verify suggestions against official documentation. AI can occasionally hallucinate incorrect configurations or outdated syntax. The combination of AI speed and human verification produces the best results.
My Workflow
A typical task looks like this:
- Describe what I want to build to AI
- Review and understand the generated code
- Verify against AWS documentation
- Test locally before deploying
- Ask AI to review the final implementation
This workflow has easily doubled my productivity for infrastructure work while helping me learn faster than documentation alone.