25 AI Shortcuts Every DevOps Engineer Should Know (But Most Don't)

Learn the 25 proven techniques to automate the boring stuff and focus on what actually matters to harness the power of AI for the present and future of most IT jobs

Look, I've been doing DevOps for over a decade and I've seen plenty of hype cycles. But after 18 months of actually using AI tools in production, some of them are legitimately game-changing. Not the "AI will replace all engineers" nonsense, but real "I just saved 4 hours of tedious work" value.

This isn't another theoretical guide. These are the techniques that experienced DevOps engineers are quietly using to get way more done with way less frustration.

What AI Actually Does Well:

• Pattern recognition at scale (logs, metrics, configurations)

• Code generation when given proper context

• Routine decision-making with clear parameters

• Data correlation across multiple systems

• Cost optimization through usage analysis

Here's a real example of AI pattern recognition that saved us hours: I fed weeks of application logs to ChatGPT with this prompt:

"Analyze these logs for patterns that correlate with performance slowdowns. 
Look for combinations of events, timing patterns, or subtle indicators 
that traditional monitoring might miss."

The AI identified that slowdowns always happened when users performed a specific sequence of actions within 30 seconds - something that would have taken weeks to discover manually.

What AI Still Sucks At:

• Understanding business context

• Strategic decision-making

• Knowing when rules should be broken

• Handling edge cases gracefully

• Working with inconsistent or poor-quality data

Code Generation Reality Check: Traditional approach - writing Terraform for monitoring setup:

# This would take 2-3 hours to write from scratch
resource "aws_cloudwatch_dashboard" "main" {
  dashboard_name = "MyApp"
  # ... hundreds of lines of configuration
}

AI-enhanced approach with proper context:

# File: complete-ecommerce-monitoring-dashboard.tf
# Comprehensive monitoring for e-commerce app with user journey tracking,
# business metrics (conversion rates, cart abandonment), and infrastructure health

# Copilot generates this entire configuration in minutes:
resource "aws_cloudwatch_dashboard" "ecommerce" {
  dashboard_name = "ECommerce-Business-Metrics"
  
  dashboard_body = jsonencode({
    widgets = [
      {
        type   = "metric"
        properties = {
          metrics = [
            ["AWS/ApplicationELB", "RequestCount", "LoadBalancer", var.alb_name],
            [".", "TargetResponseTime", ".", "."],
            ["Custom/ECommerce", "CheckoutConversionRate"],
            [".", "CartAbandonmentRate"]
          ]
          period = 300
          stat   = "Average"
          region = var.aws_region
          title  = "Business KPIs"
        }
      }
    ]
  })
}

The difference? File naming and intent comments told the AI exactly what I was building.