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Posted on • Originally published at controlmonkey.io

⚙️ 7 AI-Powered Prompts That Supercharge Your Terraform Workflow

⚙️ 7 AI-Powered Prompts That Supercharge Your Terraform Workflow

By Daniel Alfasi — Backend Developer & AI Researcher


For years, Terraform has been the backbone of Infrastructure as Code (IaC).

Now, with AI entering the workflow, engineers no longer need to spend hours troubleshooting syntax, writing repetitive modules, or combing through verbose plan outputs.

Terraform + AI brings the same revolution that developers already enjoy in their editors — directly into the world of cloud infrastructure.


🤖 LLMs for Terraform in IDEs & CLI

AI copilots are no longer confined to browser tabs — they now sit inside the tools you already use every day.

🧩 GitHub Copilot & Amazon CodeWhisperer

Autocomplete HCL, Bash, and Go tests. Suggest variable names, generate resource blocks, and explain errors inline.

🔗 GitHub Copilot | Amazon CodeWhisperer

💡 Cursor AI & Continue (VS Code / JetBrains)

Run one-shot refactors like:

“Extract these CIDRs into variables”

“Convert count loops to for_each”

Highlights hard-coded values as you type.

🔗 Cursor AI | Continue.dev

💬 OpenAI Chat in Editors

Chat about the current file or diff.

Ask things like:

“Why is this plan destroying prod?”

and get an instant summary — no context switching.

💻 Natural-Language CLI Wrappers

Tools like Warp AI let you type:

“Add S3 bucket encryption”

…and get the exact Terraform or AWS CLI command.

🔗 Warp AI

🐵 ControlMonkey KoMo — The IaC Copilot

Meet KoMo, ControlMonkey’s AI Copilot for Terraform.

KoMo helps engineers tag resources, detect drift, and flag destructive changes before merges — all within governed workflows connected to policy checks and audit logs.

🎥 See KoMo in action — Request a Demo


🧠 7 AI Prompts to Level Up Your Terraform Workflow

These prompts work with AI assistants like Cursor AI, GitHub Copilot, or Warp AI — helping you write cleaner Terraform faster, with fewer mistakes.


🧮 Prompt 1: Convert Magic Numbers into Variables

Prompt

# Highlight every hard-coded CIDR, AMI, or instance size and convert it to a variable.
# Add sensible defaults in variables.tf and environment-specific values in dev.tfvars and prod.tfvars.
# Then run: terraform validate
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Why it matters:

Hard-coding introduces fragility. Extracting values into variables improves reusability and prevents accidental rebuilds.

✅ Promotes reusable modules

✅ Catches wiring mistakes early

✅ Reduces environment drift


🏷️ Prompt 2: Tag or Label All Resources

Prompt

# Scan this folder and list any resource that lacks a tags block (AWS) or labels block (GCP/Azure).
# Show the file and line number, and generate a patch snippet for each offender.
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Why it matters:

Tagging is essential for FinOps, cost visibility, and cleanup automation.

✅ Enforces tagging compliance

✅ Improves billing insights

✅ Enables automated lifecycle management


💥 Prompt 3: Detect Destructive Terraform Code Changes

Prompt

# Given this Terraform plan output (preferably terraform show -json plan.tfplan):
# - List every resource marked for destruction or replacement (-, -/+, or delete actions).
# - Explain the cause for each.
# - Suggest safer alternatives:
#     - terraform apply -replace=RESOURCE_ADDR
#     - lifecycle { create_before_destroy = true }
#     - lifecycle { prevent_destroy = true }
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Why it matters:

Prevents outages by highlighting destructive changes and offering safer alternatives.

✅ Reduces production risk

✅ Improves plan review clarity

✅ Encourages safer lifecycle patterns


🔍 Prompt 4: AI-Powered Drift Detection

Prompt

# Given this terraform plan output (ideally JSON via terraform show -json plan.tfplan):
# - Highlight resources where current infra differs from desired config.
# - Categorize drift: console change, autoscaling, or unknown.
# - Suggest remediation for each category.
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Why it matters:

Drift detection ensures reproducibility and prevents “ClickOps chaos.”

✅ Flags console changes early

✅ Keeps IaC in sync with cloud reality

✅ Supports import/revert workflows


📋 Prompt 5: Human-Readable Terraform Plan Summaries

Prompt

# You are an expert DevOps engineer.
# Given the output of a Terraform plan:
# - Explain it in plain language.
# - List which resources are created, updated, or destroyed.
# - Keep it concise and human-readable.
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Why it matters:

Plan outputs are notoriously verbose — AI can translate them into actionable English.

✅ Improves cross-team visibility

✅ Builds confidence in IaC

✅ Simplifies code reviews


🔒 Want Prompts 6 & 7?

The next two prompts cover:

  • Security & compliance scanning with AI
  • Dependency & version drift control

👉 Read the full article here:

➡️ 7 AI-Powered Prompts That Supercharge Your Terraform Workflow →


💬 Which of these prompts will you try first?

Share your favorite Terraform + AI tricks in the comments 👇

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