Infrastructure as Code with AI

🔄 Quick Recall: In the previous lesson, you designed advanced pipeline patterns — deployment strategies, matrix builds, and conditional execution. Now you’ll learn Infrastructure as Code (IaC) — defining your servers, databases, networks, and cloud services as version-controlled code that AI helps you write, review, and maintain.

Infrastructure as Code means your entire infrastructure is defined in text files that can be versioned, reviewed, tested, and reproduced. AI reduces configuration drift by 90%+ and makes writing IaC accessible to developers who aren’t infrastructure specialists.

Generating Infrastructure Code

AI prompt for IaC generation:

Generate Terraform configuration for the following infrastructure. Cloud provider: [AWS / GCP / Azure]. Requirements: (1) [DESCRIBE — e.g., “a web application with a load balancer, 2 app servers, a PostgreSQL database, and an S3 bucket for static assets”], (2) Environment: [production / staging / development], (3) Region: [REGION], (4) Budget constraints: [ANY]. Generate: the Terraform files with proper module structure, variables for all configurable values, outputs for important resource IDs/URLs, tags on all resources, and comments explaining each resource’s purpose. Follow Terraform best practices: use data sources for existing resources, locals for computed values, and separate files for variables, outputs, and main resources.

IaC tool comparison:

Module Design

AI prompt for module structure:

Review this Terraform module and suggest improvements. Module code: [PASTE CODE]. Check for: (1) Hardcoded values that should be variables (regions, instance sizes, CIDR blocks), (2) Missing outputs (resource IDs, URLs, and ARNs that other modules might need), (3) Missing tags (cost tracking, environment, team ownership), (4) Security issues (open security groups, public S3 buckets, unencrypted resources), (5) Missing lifecycle configurations (prevent accidental deletion of stateful resources), (6) Module decomposition — should this monolithic module be split into smaller, reusable modules?

✅ Quick Check: Your Terraform module has instance_type = "t3.large" hardcoded. Why is this a problem? (Answer: Different environments need different sizes — production might need t3.xlarge, development needs t3.small. Hardcoding means you need separate modules for each environment or override the value with error-prone workarounds. The fix: variable "instance_type" { default = "t3.large" } and pass the appropriate value per environment. AI scans for hardcoded values and extracts them to variables automatically.)

State Management

AI prompt for state management:

Design a Terraform state management strategy for my team. Team size: [NUMBER]. Number of environments: [prod/staging/dev]. Infrastructure components: [LIST]. Generate: (1) remote state backend configuration (S3 + DynamoDB for locking on AWS, GCS for GCP), (2) state file organization — one state per environment, or one state per service per environment, (3) state locking configuration to prevent concurrent modifications, (4) state access controls — who can read/write which state files, (5) state migration plan if we’re currently using local state.

Environment Management

AI prompt for multi-environment IaC:

Structure my Terraform configuration for multiple environments (dev, staging, production). Current setup: [DESCRIBE — separate directories? workspaces? tfvars?]. Generate: (1) the recommended approach for my team size and infrastructure complexity, (2) shared modules that all environments use, (3) environment-specific variable files (tfvars), (4) how to promote changes from dev → staging → production safely, (5) drift detection — how to check if actual infrastructure matches the code.

Key Takeaways

• Infrastructure as Code eliminates snowflake servers — every server is defined in version-controlled code that can be reproduced exactly. AI migrates manual configurations to IaC by analyzing existing servers and generating equivalent code
• Always read terraform plan output before applying — a “destroy and recreate” on a stateful resource (database, volume) means data loss. AI reviews plan output and flags destructive operations before they happen
• Standardize IaC incrementally, not all at once: AI audits the entire codebase, generates a prioritized fix list, and refactors one module at a time while preserving exact behavior
• Every configurable value should be a variable, every useful resource attribute should be an output, and every resource should have tags — AI extracts hardcoded values and adds missing metadata automatically
• Remote state with locking prevents two team members from modifying infrastructure simultaneously — AI generates the backend configuration for your cloud provider with proper access controls

Up Next

In the next lesson, you’ll learn containerization — using AI to build, optimize, secure, and orchestrate Docker containers and Kubernetes deployments.