Terraform Module Creation AI Prompts for Cloud Engineers

Quick Answer

We help cloud engineers accelerate Terraform module creation using AI prompts. Our approach transforms vague ideas into secure, compliant, and reusable infrastructure code by applying structured prompt engineering. This guide provides the exact frameworks and examples needed to turn your AI co-pilot into a production-ready asset.

The AI-Powered Future of Infrastructure as Code

Remember the days of SSH-ing into servers to manually apply changes, praying you hadn’t missed a step? We’ve evolved from those fragile, manual processes to the declarative power of Terraform, where our infrastructure is defined, versioned, and repeatable. But as our cloud environments grow in complexity, even writing modular, reusable Terraform code can feel like a bottleneck. What’s the next logical step in this abstraction journey? It’s leveraging AI to accelerate the creation of that code, transforming how we, as cloud engineers, build and ship infrastructure.

Embracing AI prompting isn’t about replacing your expertise; it’s about augmenting it. The benefits are tangible and immediate. Imagine generating a secure, compliant, and well-structured module for an Azure Storage Account or a GCP VPC in seconds, not hours. This is about dramatically increasing speed, enforcing unwavering consistency across teams, and reducing configuration errors that often lead to security vulnerabilities or costly downtime. AI also acts as a powerful bridge, helping you quickly grasp the nuances between cloud providers by generating comparative code snippets, effectively closing knowledge gaps.

The Golden Nugget: A seasoned engineer knows that the real work isn’t just writing the code, but securing it. A powerful prompt isn’t “Create an S3 bucket.” It’s “Generate a Terraform module for a private S3 bucket with versioning, server-side encryption using a customer-managed KMS key, and a strict IAM policy that only allows access from a specific VPC endpoint.” This level of detail is where AI becomes an indispensable partner.

However, it’s crucial to set the right expectation. AI is your expert co-pilot, not an autonomous pilot. It generates an exceptional first draft, a powerful starting point, but it is not production-ready code. The engineer’s role in validation, security review, and understanding the business context remains absolutely critical. You are the architect who validates the blueprint, and the AI is the tireless apprentice who draws it for you.

The Fundamentals of Prompt Engineering for Terraform

How do you go from a vague idea to a production-ready, secure, and compliant Terraform module using an AI co-pilot? The answer isn’t magic; it’s a disciplined engineering practice applied to a new interface. Writing a prompt for an AI to generate Infrastructure as Code (IaC) is fundamentally different from asking it to write a blog post. You’re not just requesting text; you’re orchestrating a complex set of dependencies, security best practices, and cloud provider APIs. A well-structured prompt is the difference between receiving a brittle, insecure script and a robust, reusable module that saves you hours of work.

Anatomy of a High-Quality Terraform Prompt

The single most common mistake engineers make is being too vague. A prompt like “Create a Terraform module for an AWS S3 bucket” will yield a generic result that is likely insecure and incomplete. To get a module you can actually use, you must structure your prompt with the precision of a technical specification. I structure all my successful prompts around four key pillars:

• Context (The “Who”): Define the AI’s persona. Start with a clear directive like, “You are a senior DevOps engineer specializing in secure, multi-cloud infrastructure. Your code must adhere to the latest CIS benchmarks and Terraform best practices.” This sets the expected quality bar.
• Task (The “What”): Be hyper-specific about the resource and its purpose. Instead of “an S3 bucket,” specify, “Create a Terraform module for a private S3 bucket intended for storing encrypted application logs. The bucket must block all public access and enable versioning by default.”
• Constraints (The “How”): This is where you prevent future headaches. Explicitly state provider versions, required tags, naming conventions, and any organizational standards.
  • Provider Version: “Use the AWS provider version ~> 5.0.”
  • Region: “The region should be a variable, defaulting to us-east-1.”
  • Tags: “All resources must have mandatory tags: Environment, Project, CostCenter, and Owner. These should be passed as a variable map.”
  • Security: “Ensure the module enforces server-side encryption using a KMS key and denies non-SSL requests.”
• Output Format (The “Deliverable”): Don’t ask for code; ask for a complete, ready-to-use file structure. This is a critical step that many miss. Prompt for: “Provide the complete file structure for a standard Terraform module, including main.tf, variables.tf, outputs.tf, and README.md. For each file, provide the full, commented code block.”

Iterative Refinement Strategies

Your first prompt will rarely be your last. The real power of AI is unlocked through an iterative, conversational workflow. Think of it as pair programming. You’ve received the first draft; now, you guide the AI to refine it. This is where you move from generating a module to engineering one.

Let’s say the AI generated a module with a hardcoded instance type. Your follow-up prompt isn’t just a correction; it’s an improvement. You’d prompt: “Excellent. Now, refactor the ec2_instance resource to use a t3 variable for the instance type. Also, add a user_data variable to allow for custom bootstrapping scripts.” This demonstrates how you can use follow-up prompts to add features, improve flexibility, and enforce best practices. A common refinement I use is to optimize for cost and readability: “Refactor this module to use dynamic blocks for security group rules. This will reduce code duplication and make it easier to manage multiple rule sets via a single variable.” This single instruction transforms a verbose, hard-to-maintain resource into a clean, scalable piece of code.

Golden Nugget: Always ask the AI to generate variables.tf and outputs.tf alongside your main.tf. A module without defined inputs and outputs is not a module; it’s a one-off script. Forcing this structure from the beginning ensures your module is immediately reusable across your organization.

Common Pitfalls to Avoid

Even with a solid structure, a few common errors can derail your AI-assisted IaC development. These pitfalls often lead to frustrating debugging sessions where the generated code looks right but fails on terraform plan.

• Vague Requests: As mentioned, “create a VPC” is a recipe for failure. The AI will make assumptions about CIDR blocks, subnets, and gateways that will almost certainly conflict with your existing network topology. Always provide specific CIDR ranges or make them explicit variables.
• Missing Provider Versions: Forgetting to specify the provider version in your prompt can lead to generated code that uses deprecated arguments or resources. Terraform is notoriously sensitive to provider versions, and a module written for AWS provider v4 will often fail with v5. Always lock the version in your prompt.
• Forgetting Variables and Outputs: This is the most critical structural error. A generated module that has hardcoded resource names or IDs is useless. You must explicitly ask the AI to create variables for all configurable parameters (like instance type, bucket names, security group rules) and outputs for any values you need to reference elsewhere (like an instance ID or an ARN).

By mastering these fundamentals—the structured prompt, the iterative refinement process, and the awareness of common pitfalls—you transform AI from a novelty into a predictable, high-leverage tool in your IaC toolkit.

Mastering AWS: Prompts for Modular EC2, VPC, and S3 Resources

How do you ensure your AI-generated Terraform modules are not just functional, but also secure, scalable, and compliant from the very first prompt? The secret lies in moving beyond simple requests like “create a VPC” and instead adopting a structured, context-rich prompting methodology. As a cloud engineer, you know that the real value isn’t in the code itself, but in the architectural decisions embedded within it. Your prompts must reflect that expertise. This section provides battle-tested prompt templates for three of the most critical AWS resources, designed to generate production-grade modules that adhere to security best practices and modern architectural patterns.

Prompting for Secure VPC Modules

A Virtual Private Cloud (VPC) is the foundation of any AWS deployment, and getting its security posture right is non-negotiable. A naive prompt will give you a flat network; an expert prompt builds a fortress. When engineering a prompt for a VPC module, you must explicitly define the network topology, traffic flow, and security boundaries. The goal is to generate Infrastructure as Code that enforces the principle of least privilege by default.

Consider this prompt structure, which has been refined through countless deployments:

Prompt: “Generate a production-ready Terraform module for an AWS VPC. The module must adhere to the following specifications:

• CIDR Block: Variable-based, with a secure default (e.g., 10.0.0.0/16).
• High Availability: Create public and private subnets across at least 3 Availability Zones.
• Internet & NAT: Provision an Internet Gateway for public subnets and NAT Gateways (one per AZ) for private subnets to allow outbound traffic.
• Security: Generate separate security group modules for a web tier (allowing HTTP/HTTPS from the ALB) and an application tier (allowing traffic only from the web tier on specific ports). Crucially, avoid opening port 22 (SSH) to the world (0.0.0.0/0).
• Output: The module should output all subnet IDs, security group IDs, and VPC ID for use by other modules.”

This prompt succeeds because it pre-empts common security flaws. It forces the AI to think about network segmentation and secure access patterns. A golden nugget to add in your review of the generated code is to check for the creation of VPC Flow Logs. You can add a line to the prompt: “Optionally enable VPC Flow Logs to a specified CloudWatch Log Group ARN.” This single instruction elevates the module from a basic setup to a security-conscious, auditable foundation.

Generating Scalable EC2 Auto-Scaling Groups

Static EC2 instances are relics of a bygone era. Modern applications demand elasticity. When prompting for an Auto-Scaling Group (ASG), your focus should be on the launch template, scaling policies, and integration with a load balancer. The prompt’s value lies in its ability to codify best practices for resilience and performance.

Here’s a prompt designed to produce a robust, scalable compute layer:

Prompt: “Write a Terraform module for a scalable web application tier. The module should include:

• Launch Template: Use a data source to find the latest Amazon Linux 2 AMI. Define instance type as a variable. Attach an IAM instance profile with a policy that allows read-only access to S3 buckets prefixed with ‘my-app-config’.
• Auto-Scaling Group (ASG): Configure the ASG to span the private subnets created by our VPC module. Set a desired capacity, minimum, and maximum number of instances.
• Health Checks: Implement ELB health checks with a grace period of 300 seconds.
• Load Balancer Integration: The ASG must be associated with an Application Load Balancer (ALB) Target Group. The prompt should generate the ALB, listener, and target group resources, configured for HTTPS traffic.
• Scaling Policies: Create a dynamic scaling policy that adds instances when the average CPU utilization across the group exceeds 70%.”

By specifying the IAM role permissions directly in the prompt, you prevent the common error of over-privileged instances. You’re not just asking for an ASG; you’re architecting a secure, multi-tiered application. The instruction to use a data source for the AMI ensures your infrastructure remains current without manual intervention—a small detail that prevents significant technical debt.

Creating Intelligent S3 Bucket Modules

S3 is deceptively simple, yet it’s a frequent source of security breaches and spiraling costs. An “intelligent” S3 module is one that is secure by default, cost-optimized, and compliant with data governance policies. Your prompts must be prescriptive about these aspects to avoid generating buckets that are publicly accessible or racking up unnecessary storage costs.

Use this prompt to generate a secure and compliant S3 module:

Prompt: “Generate a Terraform module for an S3 bucket that will store application logs. The module must enforce the following security and lifecycle configurations:

• Block Public Access: The bucket must have block_public_acls, block_public_policy, and ignore_public_acls all set to true.
• Encryption: Enforce server-side encryption using AWS KMS with a customer-managed key (CMK) passed as a variable.
• Versioning: Enable bucket versioning to protect against accidental deletions.
• Lifecycle Policy: Create a variable-based lifecycle rule to transition objects to Glacier Deep Archive after 90 days and permanently delete them after 365 days.
• IAM Policy: Generate a separate IAM policy document that grants read/write access to the bucket but is scoped to a specific role ARN passed as an input variable. Do not embed the policy inline on the bucket.”

This prompt directly addresses the most critical S3 controls. It explicitly forbids public access, mandates encryption, and builds in cost savings through lifecycle management. The instruction to generate a separate, scoped IAM policy is a critical security practice. It ensures that access is granted based on identity, not just network location, which is the cornerstone of modern cloud security. When you review the output, you’ll find you have a module that is not just a storage container, but a well-governed data store.

Building on Azure: Prompts for Resource Groups, AKS, and Networking

How do you ensure your Azure infrastructure isn’t just a collection of resources, but a cohesive, secure, and maintainable system? The answer lies in moving beyond basic resource definitions and prompting for architectural patterns. When you’re managing complex environments on Azure, the difference between a functional setup and a robust one often comes down to how you structure your foundational elements. Let’s dive into crafting AI prompts that generate sophisticated, production-ready Azure infrastructure code.

Structuring Resource Groups and Tagging Strategies

A well-defined resource group is the bedrock of any Azure deployment. It’s more than just a container; it’s your primary unit of billing, access control, and lifecycle management. A common mistake is to create resource groups with inconsistent tagging, leading to a nightmare for cost allocation and governance down the line. Your prompt should enforce a standard.

When you’re building a module for a new application, you need to ensure every single resource—be it a database, a virtual machine, or a storage account—carries the correct metadata. This is where a prompt that generates a “tagging module” or enforces tags at the resource group level becomes invaluable. It’s about building governance directly into your code from day one.

Here’s a prompt structure that works well for this:

Prompt: “Generate a Terraform module for an Azure Resource Group that enforces a standardized tagging strategy. The module should accept variables for project_name, environment, and cost_center. It must apply these tags, plus an owner tag, to the resource group itself. Crucially, design the module so that any child resources created within it (like a virtual network or storage account) can easily inherit these same tags. Include an output for the full map of tags to be used by other resources.”

The generated code will typically define the azurerm_resource_group resource with a tags attribute constructed from the input variables. The real value, however, is in the output. A well-structured module will output the tags as a map, allowing other modules to reference module.resource_group.tags and ensure perfect consistency. This simple pattern eliminates “tag drift” and makes your Azure environment queryable and auditable by design.

Automating Azure Kubernetes Service (AKS) Deployments

Deploying a production-grade AKS cluster is a multi-faceted task that goes far beyond a single azurerm_kubernetes_cluster resource. You need to think about node pools for different workloads, Kubernetes RBAC for security, and network policies to control pod-to-pod traffic. A single, monolithic prompt often leads to a brittle, unmanageable module. The expert approach is to use a conversational, iterative process with your AI.

Start by defining the core cluster properties. Then, layer on complexity. This is a “golden nugget” of experience: always separate your system node pool from your user node pools. This allows you to maintain cluster stability by isolating system services from your application workloads.

Consider this multi-step prompting strategy:

1. The Foundation:

   “Write the Terraform HCL for an azurerm_kubernetes_cluster resource. Configure it with Azure RBAC enabled and a default system node pool with vm_size ‘Standard_D2s_v3’ and node_count of 3. Output the cluster’s id and kube_config.”

2. Layering on Node Pools:

   “Now, add a separate azurerm_kubernetes_cluster_node_pool resource for user workloads. Name it ‘userpool’, use vm_size ‘Standard_D4s_v3’, and configure it for autoscaling with a minimum of 1 and maximum of 10 nodes. Ensure this pool is tainted to prevent system pods from being scheduled on it.”

3. Securing the Cluster:

   “Refine the previous code to integrate Azure AD integration for Kubernetes RBAC. Also, add a azure_policy_enabled block to enforce cluster-level policy compliance. Finally, generate a kubernetes_network_policy resource that denies all ingress traffic by default, allowing only explicitly whitelisted connections.”

By building the AKS cluster in this modular, step-by-step fashion, you maintain control and clarity. The final output is a comprehensive module that handles not just the cluster’s existence, but its security, scalability, and operational readiness.

Azure Virtual Network (VNet) Peering and Subnets

The hub-and-spoke network topology is the gold standard for cloud networking in 2025, providing clear separation of concerns, centralized security, and optimized routing. Manually peering VNets and creating subnets with Network Security Group (NSG) associations is tedious and error-prone. AI-generated code excels at this level of repetitive, rule-based complexity.

Your goal is to prompt for a reusable module that can build the hub, and then a separate module (or a parameterized one) to create spokes and attach them. The key is to manage the dependencies correctly—peering won’t work until both networks exist.

A highly effective prompt for this task looks like:

Prompt: “Create a Terraform module to establish a hub-and-spoke network topology in Azure. It should create three resources: a ‘hub-vnet’, a ‘spoke-vnet’, and a ‘firewall-subnet’ within the hub. The hub VNet should have a subnet named ‘AzureFirewallSubnet’. Generate the azurerm_virtual_network_peering resources in both directions (hub-to-spoke and spoke-to-hub) to connect them. Ensure the peering allows gateway and forwarded traffic. As a golden nugget, add a route table resource that directs all traffic from the spoke’s subnets to the Azure Firewall’s private IP in the hub.”

The AI will generate the HCL for the two azurerm_virtual_network resources, the peering resources with their allow_forwarded_traffic and allow_gateway_transit attributes set correctly, and the azurerm_route_table to enforce the centralized inspection model. This prompt not only builds the network but also encodes a critical security best practice: forcing traffic through a central inspection point. When you review the code, you’ll see a clean, interconnected set of resources that correctly implements a complex architectural pattern, saving you hours of manual configuration and potential debugging.

Google Cloud Platform (GCP): Prompts for GKE, Cloud Storage, and IAM

Building secure and scalable infrastructure on GCP requires more than just knowing the resource types; it demands a deep understanding of how identity, networking, and compute interact. When you’re using AI to generate Terraform, your prompts must reflect this interconnected reality. A simple request like “create a GKE cluster” will give you a default cluster that is likely insecure and not production-ready. The real value comes from prompting the AI to implement specific security postures and architectural patterns that you would build by hand.

Generating GKE Clusters with Workload Identity

The single most important security feature for GKE is Workload Identity. It allows your Kubernetes service accounts to act as IAM service accounts, eliminating the need for static service account keys. This is a non-negotiable best practice. When prompting for a GKE module, you must explicitly instruct the AI to enable and configure this feature, including the necessary IAM bindings.

A common pitfall I’ve seen in junior engineers is creating the cluster and the IAM binding in separate, unrelated prompts. This often leads to timing issues where the node pool isn’t ready when the binding is applied. A robust prompt forces the AI to consider these dependencies.

Prompt Example:

Generate a Terraform module for a GKE cluster named “secure-app-cluster” in the “us-central1” region. The module must use a dedicated VPC and subnet. Crucially, enable Workload Identity on both the cluster and node pool levels. The prompt should also create an IAM service account named “gke-sa” and output the necessary google_service_account_iam_binding resource to allow the Kubernetes default service account to impersonate this IAM SA. Ensure the node pool uses a modern machine type like e2-medium and auto-scaling is enabled.

This prompt yields a module that is secure by default. The AI will generate the google_container_cluster resource with workload_identity_config, the google_container_node_pool with the same, and the critical IAM binding that connects them. You’re not just getting a cluster; you’re getting a secure identity boundary for your applications.

Cloud Storage and Bucket IAM Bindings

When working with Cloud Storage, the key is to separate the bucket resource from its access controls. This follows the principle of least privilege and makes your infrastructure auditable. Instead of embedding IAM roles directly in the bucket definition, the best practice is to create a dedicated IAM policy resource. This allows you to manage bucket access independently of the bucket itself.

I once audited a project where developers had to remove a critical bucket because a misconfigured IAM binding prevented them from deleting a stale object. The root cause was a lack of separation between the bucket and its policies. Your prompts should prevent this by generating modular, decoupled code.

Prompt Example:

Write a Terraform module for a GCS bucket named project-data-lake-unique-id with Uniform Bucket-Level Access enabled. The bucket should be versioned and have a lifecycle rule to delete objects after 365 days. Generate a separate google_storage_bucket_iam_binding resource that grants the roles/storage.objectViewer role to a specific list of user emails passed as a variable. Do not grant any roles directly on the bucket resource.

The AI will correctly generate the google_storage_bucket resource with the specified features and a separate google_storage_bucket_iam_binding resource. This structure is far more manageable and secure. It explicitly prevents public access by default and ensures you are thinking about who can access the data, not just that the bucket exists.

VPC Native Networking and Firewall Rules

GCP’s VPC-native networking model uses alias IP ranges, which is a significant shift from the old flow-based models. When prompting for VPCs and firewall rules, you need to be precise about these modern features. More importantly, you should prompt for firewall rules that are dynamic, based on service account tags or network tags, rather than static IP addresses. This allows your infrastructure to scale and change without manual firewall updates.

This is a critical insight: firewall rules tied to tags or service accounts are self-healing. When a new instance is created with the right tag, it automatically gets the correct network access. Prompting for this demonstrates a deep understanding of cloud networking.

Prompt Example:

Create a Terraform module for a GCP VPC named “app-vpc” with a single private subnet in “us-east1”. The module should also generate a firewall rule named “allow-internal-https” that allows ingress traffic on port 443. Instead of using source IP ranges, the rule must be scoped to instances carrying the network tag backend-service. Additionally, create a second rule that allows ingress from the GCP health check IP ranges for the load-balancer tag.

The resulting Terraform will include the google_compute_network and google_compute_subnetwork resources, plus two google_compute_firewall resources. The source_tags or target_tags attributes will be used instead of source_ranges, creating a robust and dynamic network security posture. This is the kind of architectural thinking that separates basic code generation from true infrastructure engineering.

Advanced Module Patterns: Dynamic Blocks, Count, and For-Each

You’ve mastered the basics of writing a Terraform module for a single resource. But what happens when your infrastructure needs to scale? A static module that only creates one ingress rule or one tag is brittle. It forces you to duplicate code or create an explosion of micro-modules. The real power of Terraform, especially when augmented by AI, lies in creating flexible, reusable components that adapt to your inputs. This is where dynamic blocks, count, and for_each become your most powerful tools for building truly robust infrastructure as code.

Prompting for Dynamic Block Generation

One of the most common challenges in Terraform is handling nested blocks that need to be generated dynamically, like ingress rules for a security group or tags for a resource. Hardcoding these is a maintenance nightmare. The solution is to use a dynamic block, and the perfect use case for an AI co-pilot. You can describe the logic, and the AI will generate the correct HCL structure.

A “golden nugget” for production systems is to always define your variable structure first. Don’t ask the AI to invent the variable schema. Give it a well-defined map or list, and ask it to write the dynamic block that consumes it. This ensures your module’s interface is intentional and robust.

Here’s a prompt that demonstrates this approach for generating security group ingress rules:

Prompt: “Create a Terraform module for an AWS security group. The module should accept a variable named ingress_rules which is a map of objects. Each object should have keys for from_port, to_port, protocol, and description. Write the dynamic 'ingress' block inside the aws_security_group resource to iterate over this map and create a rule for each entry. Also, generate the corresponding variable 'ingress_rules' definition with the correct type.”

The AI will produce a module that looks something like this:

variable "ingress_rules" {
  description = "Map of ingress rules to create"
  type = map(object({
    from_port   = number
    to_port     = number
    protocol    = string
    description = string
  }))
  default = {}
}

resource "aws_security_group" "main" {
  name = "dynamic-sg"

  dynamic "ingress" {
    for_each = var.ingress_rules
    content {
      from_port   = ingress.value.from_port
      to_port     = ingress.value.to_port
      protocol    = ingress.value.protocol
      description = ingress.value.description
      cidr_blocks = ["0.0.0.0/0"] # Or pass as another variable
    }
  }
}

This pattern is infinitely more reusable. You can now create a security group with any number of rules just by passing a map in your root module, without ever touching the security group module’s code again.

Using Count and For-Each in Modules

When you need to create multiple instances of a resource, count and for_each are your primary tools. The difference is critical: count creates a numbered list of resources, while for_each creates a map of resources, indexed by a key. This distinction is the key to stable, predictable infrastructure.

When prompting for this, your goal is to teach the AI to prefer for_each for its stability. If you remove an item from the middle of a list used by count, Terraform will want to destroy and recreate all subsequent resources. for_each avoids this entirely.

Prompt: “Write a Terraform module that creates multiple AWS IAM users. The module should accept a variable user_names which is a set of strings. Use the for_each meta-argument to loop over this set and create an aws_iam_user resource for each name. Output the ARN of each created user as a map, keyed by the user’s name.”

This prompt explicitly asks for a set (which is a type of list) and instructs the use of for_each. The resulting code will be resilient to changes in the user list order.

variable "user_names" {
  type = set(string)
}

resource "aws_iam_user" "this" {
  for_each = var.user_names
  name     = each.value
}

output "user_arns" {
  description = "The ARNs of the created IAM users"
  value       = { for k, v in aws_iam_user.this : k => v.arn }
}

The for_each prompt is a perfect example of where AI excels. It’s a common pattern, but the syntax can be tricky. Getting it right from the start prevents the state management headaches that plague many Terraform users.

Creating Composite Data Sources

Your modules become truly intelligent when they can make decisions based on external, dynamic information. Instead of hardcoding an AMI ID or a specific image version, you can prompt the AI to generate a data source that looks up the correct resource based on filters. This is the foundation of self-healing, version-agnostic infrastructure.

The key here is to prompt the AI to combine data sources and use Terraform’s built-in functions to find the single, correct value.

Prompt: “Generate a Terraform configuration that dynamically finds the latest Amazon Linux 2 AMI. Use the aws_ami_ids data source with a filter for the name pattern ‘amzn2-ami-hvm-*-x86_64-gp2’. Then, use the sort() function on the returned list of IDs and select the last element to ensure you get the most recent one. Output the selected AMI ID.”

This prompt forces the AI to move beyond a simple data source lookup. It requires sorting and selection logic, which is a common real-world requirement.

data "aws_ami_ids" "amazon_linux_2" {
  owners = ["amazon"]

  filter {
    name   = "name"
    values = ["amzn2-ami-hvm-*-x86_64-gp2"]
  }
}

output "latest_ami_id" {
  value = element(sort(data.aws_ami_ids.amazon_linux_2.ids), length(data.aws_ami_ids.amazon_linux_2.ids) - 1)
}

By using this composite data source pattern, your module will automatically pick up new AMI releases without any code changes, ensuring your instances are always patched and secure. This is a hallmark of a mature, production-ready Terraform module.

Testing and Validation: Prompts for Unit and Integration Testing

You’ve written the Terraform code. It looks clean, it follows best practices, and it provisions the infrastructure perfectly… on the first try? If you’re not testing your modules, you’re just hoping they work. In 2025, shipping untested infrastructure is a recipe for production outages and costly rollbacks. The real power of AI isn’t just generating code; it’s generating the safety net around that code.

Think of your AI co-pilot as your dedicated QA engineer. It can instantly scaffold unit tests, create complex mock data for policy validation, and even document your module for you. This transforms testing from a tedious afterthought into an integrated, automated part of your workflow. Let’s look at the prompts that make this a reality.

Generating Terraform Test (.tftest) Files

Unit testing in Terraform is no longer a nice-to-have; it’s a core competency. The terraform test framework allows you to validate your module’s logic in isolation before it ever touches a cloud provider. Your goal is to prompt the AI to become a testing specialist, creating assertions that check your variables, mocks, and outputs under different conditions.

A common mistake is only testing the “happy path.” A robust prompt will explicitly ask for edge cases. This is where your experience matters. You know that someone will eventually pass an empty string where a CIDR block is expected. Your prompt should anticipate this human error.

Golden Nugget: Don’t just ask for a test file. Ask the AI to generate a suite of test files. One for happy-path configurations, another for negative testing (e.g., ensuring an invalid AMI ID causes a plan failure), and a third for testing optional variables by passing null values. This approach forces the AI to think about module resilience, not just basic functionality.

Here is a prompt designed to generate a comprehensive test suite for a generic AWS EC2 module:

Prompt:

Generate a Terraform test suite in a file named `main.tftest.hcl` for the following module.

The module `main.tf` creates an `aws_instance` with these variables:
- `instance_name` (string, required)
- `instance_type` (string, required, default is "t3.micro")
- `ami_id` (string, required)
- `tags` (map of strings, optional)

The module outputs the instance's `id` and `private_ip`.

Your test suite must include:
1. A test named "test_default_values" that verifies the default `instance_type` is applied correctly.
2. A test named "test_custom_tags" that passes custom tags and asserts they are present on the resource.
3. A test named "test_invalid_ami" that uses a mock provider to simulate a plan failure when an invalid AMI format is provided.
4. A test named "test_output_validation" that checks if the output `instance_id` is a non-empty string starting with "i-".

Creating Sentinel/OPA Policy Mocks

Before any infrastructure is deployed in an enterprise, it must pass governance checks defined in tools like Sentinel (for Terraform Cloud/Enterprise) or Open Policy Agent (OPA). Testing these policies requires realistic mock data that simulates a Terraform plan. Manually crafting these mocks is tedious and error-prone.

This is a perfect task for an AI. You can provide the policy’s logic and the module’s context, and the AI will generate the exact JSON structure needed for a sentinel test or opa test file. This dramatically speeds up the feedback loop between writing a policy and validating it.

Prompt:

I need to create a mock Terraform plan JSON file to test the following Sentinel policy for my organization.

**Policy Rule:** All S3 buckets must have encryption enabled (`server_side_encryption_configuration` is present) and block public access (`block_public_acls` is true).

**Task:** Generate the `mock-tfplan.json` file content. The mock should include two resources:
1. A `aws_s3_bucket` resource named `good_bucket` that *complies* with the policy (has encryption and public access block).
2. A `aws_s3_bucket` resource named `bad_bucket` that *violates* the policy (no encryption, public access allowed).

Ensure the JSON structure is valid and follows the standard Terraform plan format for resource changes.

Automating Documentation Generation

A module is only as good as its documentation. If developers don’t know how to use it, they won’t. Manually updating a README.md every time you add a variable is a chore that developers are notorious for skipping. Automating this ensures your documentation is always accurate and builds trust in your module.

The key is to prompt the AI to extract information directly from your code and structure it into a user-friendly format. This is a classic “read-the-code, write-the-docs” task where AI excels.

Prompt:

Based on the Terraform module code below, generate a professional `README.md` file.

The README must include:
1. A brief "Description" section explaining the module's purpose.
2. A "Usage" section with a clear, copy-pasteable example of how to call the module.
3. An "Inputs" table that automatically lists all variables from the code, including their `description`, `type`, `default` value, and whether they are `required`.
4. An "Outputs" table listing all outputs from the code and their descriptions.

**Terraform Module Code:**
[Insert your `main.tf` or `variables.tf` and `outputs.tf` code here]

By integrating these prompts into your workflow, you’re not just writing infrastructure code faster; you’re engineering a more reliable, maintainable, and collaborative system. You’re demonstrating true expertise by building the guardrails that protect your production environment, turning your AI co-pilot into the most diligent member of your team.

Conclusion: Integrating AI into Your DevOps Workflow

We’ve journeyed from crafting simple, single-resource prompts to architecting complex, enterprise-grade modules using dynamic blocks and for_each. The core lesson is that effective prompt engineering isn’t about magic; it’s a structured process. It starts with a clear intent, evolves through iterative refinement, and culminates in a piece of infrastructure code that you, the expert, have validated. You’ve learned to guide the AI from basic boilerplate to generating sophisticated patterns that adhere to cloud best practices, like separating IAM policies from resource definitions.

From Individual Prompts to a Team Knowledge Base

Your individual expertise is powerful, but a team’s collective knowledge is a force multiplier. The most significant leap in productivity comes from treating your successful prompts as valuable assets. Don’t let those “golden nuggets” of prompt logic live in scattered chat logs or personal notes.

• Centralize: Create a shared repository (e.g., a dedicated Git repo, Confluence page, or internal wiki) for your team’s most effective prompts.
• Standardize: Develop templates for common resources (S3 buckets, GKE clusters, Azure subnets) that include your non-negotiable security and tagging requirements.
• Collaborate: Encourage a culture of peer review not just for the generated code, but for the prompts themselves. A small tweak to a prompt can save hours of refactoring later.

Golden Nugget: The most valuable prompts aren’t the ones that generate code the fastest; they’re the ones that generate the most correct-by-default code, embedding your organization’s specific security posture and compliance rules from the very first output.

The Next Frontier: AI-Native Infrastructure

Looking ahead to the rest of 2025 and beyond, the line between your editor and the AI will blur entirely. We’re moving toward a reality where AI isn’t just a separate tool you prompt, but an integrated co-pilot directly within your IDE and CI/CD pipelines. Imagine your VS Code extension proactively suggesting a more secure configuration for a resource you’re defining, or your pull request pipeline automatically generating a comprehensive set of test cases for the Terraform changes you’ve proposed. The future of cloud engineering isn’t about choosing between human or AI; it’s about mastering the synergy of both. Start building that collaborative workflow today, and you’ll be leading the charge tomorrow.

Performance Data

Frequently Asked Questions

Q: Can AI write production-ready Terraform code

AI generates a robust first draft, but it is not production-ready. You must validate logic, review security settings, and test the code before deployment

Q: What is the most important part of a Terraform prompt

Constraints are key. Specifying provider versions, mandatory tags, and security standards (like CIS benchmarks) ensures the output aligns with your organization’s policies

Q: Does using AI prompts replace the need for Terraform expertise

No. AI is an augmenting tool. Expertise is required to architect the solution, refine the prompts, and verify the generated code against real-world infrastructure requirements