Best AI Prompts for Infrastructure as Code with Cursor

Quick Answer

We’ve analyzed the provided text on AI-assisted Infrastructure as Code. Our core finding is that effective IaC generation in Cursor relies on moving beyond generic requests to structured, context-aware prompting. This guide focuses on mastering these strategies to reduce documentation lookups and accelerate deployment.

Benchmarks

Revolutionizing IaC with AI-Assisted Development

Ever feel like you spend more time with the AWS CloudFormation documentation open in one browser tab than you do actually writing code? You’re not alone. For years, a frustrating bottleneck has plagued Infrastructure as Code (IaC) development: the constant, context-killing friction between writing a resource and remembering its exact schema. Whether you’re defining a complex VPC in Terraform or a nested stack in CloudFormation, the workflow is often the same. You write a few lines, pause to verify the correct property for an IAM role, switch tabs to the provider’s API reference, and lose your train of thought. This cognitive overhead doesn’t just slow you down; it introduces subtle errors that can derail entire deployments.

This is where the paradigm shifts. Enter Cursor, an AI-first code editor that moves beyond simple code generation. The real power isn’t just asking an LLM to “write an S3 bucket”; it’s leveraging its deep understanding of cloud provider schemas for intelligent, context-aware autocompletion. Instead of just predicting the next variable name, Cursor’s AI can suggest the entire block of required properties for an aws_instance, pre-fill standard tags, and even validate against the latest API specifications in real-time. It’s the difference between a generic copy-paste and having an expert pair-programmer who has the entire AWS, Azure, and GCP documentation memorized.

This guide is your roadmap to mastering that expert partnership. We will move beyond basic prompts and dive into actionable strategies specifically designed to leverage Cursor’s AI Autocomplete. You’ll learn how to craft prompts that slash your API documentation lookups, accelerate your infrastructure deployment velocity, and help you build more robust, error-free cloud environments.

Mastering the Fundamentals: Core Prompting Strategies for IaC

The difference between a developer who wrestles with AI and one who commands it lies in the fundamentals. Getting Cursor to generate production-ready Infrastructure as Code isn’t about magic; it’s about clear communication. Think of it less like a search engine and more like a junior engineer who has memorized every cloud provider’s documentation but needs you to provide the architectural blueprint. Mastering these core strategies will transform your workflow from trial-and-error to a predictable, high-velocity process.

The “Context-Aware” Prompt: Your Blueprint for Success

The single biggest mistake developers make is asking for something without providing the necessary context. A prompt like “Create an EC2 instance” is ambiguous and will yield a generic, often insecure result. The AI doesn’t know if you’re building a quick dev sandbox or a resilient production component. To get a useful output, you must act as the architect.

Your prompt needs to answer three questions: What are you building, where are you building it, and how does it need to connect to the rest of your system? A context-aware prompt specifies the target cloud provider (AWS, Azure, GCP), the desired outcome (e.g., “a highly available web server”), and the specific IaC language (Terraform HCL, AWS CDK, Pulumi).

For example, instead of the generic request, try this:

Prompt: “Using Terraform HCL, provision a highly available web server tier for our user-facing application. This should be an Auto Scaling Group of t3.medium instances running Amazon Linux 2023, placed across two private subnets in a pre-existing VPC. The instances must be fronted by an Application Load Balancer.”

This prompt provides the AI with the necessary constraints and relationships, resulting in a configuration that is not just syntactically correct, but architecturally sound.

Iterative Refinement vs. One-Shot Generation

Resist the urge to ask for a massive, monolithic configuration file in a single request. This approach is brittle, difficult to review, and often fails due to context window limitations or logical errors. The most effective strategy is to build your infrastructure in layers, just as you would when coding manually.

Start by prompting for the skeleton structure. Ask Cursor to generate the high-level resource groups, variable definitions, and core networking components. Once you have that foundation, you can then zoom in and refine specific resource blocks.

Consider this workflow:

1. Skeleton Prompt: “Generate the basic file structure and main.tf for a Terraform project that will deploy a three-tier web application on AWS.”
2. Resource-Specific Prompt: (In a new file or block) “Now, write the aws_db_instance resource for a PostgreSQL database. It should be a db.t3.micro, use the latest PostgreSQL version, and reference the database credentials from a secure AWS Secrets Manager secret.”
3. Refinement Prompt: “Add a aws_security_group rule to the web server’s security group that allows inbound HTTPS traffic only from the ARN of the Application Load Balancer we defined earlier.”

This iterative approach allows you to review, test, and validate each component, ensuring the final result is robust and error-free.

Leveraging Comments as Prompts: The Cursor Superpower

This is where Cursor truly shines and the workflow diverges from traditional AI interaction. You don’t always need to open a separate chat window. Often, the most powerful prompt you can write is a simple, descriptive comment directly in your code editor. Cursor’s autocomplete is designed to read the code around your cursor, including comments, and generate the corresponding resource block.

This technique is incredibly efficient for scaffolding and filling in boilerplate. The process is simple:

1. Write a clear, declarative comment describing the resource you need.
2. Press Ctrl+Enter (or Cmd+Enter) to trigger the inline autocomplete.

Example in action:

You’re building out your network layer in a new Terraform file. You type the following comment:

# Create an AWS VPC with public and private subnets in us-east-1.
# The VPC should have DNS support enabled.

After hitting Ctrl+Enter, Cursor will generate the aws_vpc resource, likely including attributes for enable_dns_support and enable_dns_hostnames. It may even generate the subsequent aws_subnet resources for you, correctly referencing the new VPC’s ID. This turns a tedious documentation lookup process into a single keystroke.

Golden Nugget: The most effective comments are specific. A comment like # Create an S3 bucket is good, but # Create a private S3 bucket with versioning and server-side encryption enabled for storing application logs is infinitely better. The more detail you provide in the comment, the more “opinionated” and production-ready the autocomplete will be.

Defining Constraints Early: Guardrails for Your Cloud Architecture

In a professional environment, you’re not just writing code; you’re enforcing organizational standards for security, compliance, and cost. The most efficient way to ensure your AI-generated code adheres to these standards is to define the constraints directly in your prompt. This prevents you from having to manually refactor the output later.

By baking your rules into the request, you shift the AI’s output from “possible” to “approved.”

Examples of constraint-driven prompts:

• Security: “Generate an aws_s3_bucket resource, but ensure it blocks all public access by default and enforces TLS encryption.”
• Cost: “Write the Terraform for an EKS cluster, but use Graviton-based node groups (t4g instances) to optimize for cost-efficiency.”
• Compliance: “Create an AWS security group for our database, but ensure it does not allow ingress on port 3306 from the 0.0.0.0/0 CIDR block. Only allow traffic from our application security group.”

When you explicitly state these rules, the AI acts as a partner in enforcing best practices, generating code that is not only functional but also aligned with your team’s governance policies from the very first draft.

AWS Resource Generation: From EC2 to Complex Networking

The real power of AI-assisted infrastructure code isn’t just generating a single resource; it’s about architecting an entire cloud environment through conversational intent. When you’re staring at a blank .tf file in Cursor, the goal is to translate your architectural diagram directly into deployable code, bypassing the tedious cross-referencing of AWS documentation. This is where you move from a developer to a cloud architect, using prompts to define relationships and enforce best practices automatically.

Compute and Storage Patterns: Building the Foundation

Let’s start with the building blocks. Instead of manually looking up the exact syntax for instance_type or bucket_encryption, you can guide the AI with context-rich instructions. This approach ensures your resources are not only created but are also tagged correctly for cost allocation and secured according to common compliance frameworks.

For a standard EC2 instance, a basic prompt gets you a server, but an expert prompt gets you a production-ready resource. Consider this example:

Prompt: “Generate Terraform code for an aws_instance running Amazon Linux 2. Use a t3.micro for cost efficiency in a non-production environment. Crucially, apply a standard set of tags: Environment, Project, and Owner. Also, associate it with a security group that only allows SSH from my office IP, which is 203.0.113.0/24.”

The AI will generate the aws_instance resource, the aws_security_group, and the ingress rule, all linked together. It understands the relationship between the instance and its security context without you needing to explicitly write the vpc_security_group_ids argument first; it anticipates the dependency.

Similarly, for S3, you can enforce security and lifecycle management from the outset:

Prompt: “Create an aws_s3_bucket resource with versioning and server-side encryption enabled using AES256. Add a lifecycle rule to transition objects to Glacier after 90 days and expire them after 365 days. Name the bucket acme-logs-unique-id.”

This single prompt generates a secure, cost-effective storage configuration that adheres to best practices by default. You’re not just creating a bucket; you’re implementing a data retention policy.

Networking and VPC Architecture: Designing for Scale

Networking is often where complexity skyrockets. Manually defining VPCs, subnets, route tables, and gateways is error-prone. The key here is to prompt for “best practice” configurations, forcing the AI to generate a robust, multi-tiered architecture.

Instead of asking for a VPC, ask for a production-grade network foundation:

Prompt: “Generate a Terraform configuration for a standard three-tier VPC architecture. Create a VPC with public and private subnets across two availability zones. Include an Internet Gateway for public subnets, a NAT Gateway for private subnet egress, and the necessary route tables. Ensure all resources are properly tagged.”

The AI will generate a complex, interconnected setup that you can deploy immediately. It understands that public subnets need an Internet Gateway, private subnets need a route to a NAT Gateway, and that these components require specific route table associations. This prompt saves hours of architectural planning and syntax lookups.

Golden Nugget: When designing networking, explicitly ask the AI to “spread subnets across two availability zones.” This simple instruction forces the AI to generate count or for_each loops, creating a resilient, multi-AZ setup instead of a fragile single-AZ configuration. This is an architectural decision embedded directly in your prompt.

Database and RDS Configurations: Ensuring Reliability and Performance

Database instances are critical and often have complex configuration needs for backups, high availability, and performance tuning. Manually ensuring you’ve enabled multi_az or set the correct backup_retention_period can lead to mistakes.

You can instruct the AI to build a resilient RDS instance by simply stating your reliability requirements:

Prompt: “Write the Terraform for a production-ready aws_db_instance (MySQL 8.0). It must be a db.t3.small, deployed across multiple AZs for high availability, have a 14-day backup retention period, and enable automated minor version upgrades. Use a custom parameter group that sets the innodb_buffer_pool_size to 512MB.”

The AI will generate the aws_db_instance, the aws_db_parameter_group, and correctly link them. It automatically includes parameters like multi_az = true, backup_retention_period = 14, and auto_minor_version_upgrade = true without you having to look up their names. You’ve translated a set of business requirements (reliability, cost, performance) directly into technical configuration.

Handling Dependencies: The Magic of Interconnected Resources

The true leap in productivity comes when the AI understands and generates dependencies between resources. Manually tracking resource IDs (like aws_security_group.web.id and referencing it in an aws_instance) is a classic source of errors.

The solution is to prompt sequentially and contextually. First, define the dependency:

Prompt 1: “Generate Terraform for an aws_security_group named web-sg that allows inbound HTTP (port 80) and HTTPS (port 443) from anywhere.”

Once that code is generated, you follow up in the same conversation:

Prompt 2: “Now, create an aws_launch_template that uses this security group. The template should use an Amazon Linux 2 AMI and a t3.micro instance type.”

Because you’re working within an AI-assisted editor like Cursor, the context is maintained. The AI knows you just defined web-sg and will automatically generate the vpc_security_group_ids = [aws_security_group.web.id] argument within the launch template. It correctly references the Terraform resource name, not a static ID, ensuring your infrastructure code is dynamic and reusable. This ability to chain prompts and maintain context is what transforms AI from a simple code generator into a genuine architectural partner.

Azure & GCP Prompts: Cross-Cloud Consistency

Managing infrastructure across multiple cloud providers is a common reality for modern engineering teams, but it often introduces significant cognitive overhead. Each cloud has its own terminology, API structures, and security models. What’s a “Project” in GCP is a “Subscription” in Azure. How do you maintain a consistent security posture when Azure uses roleAssignments and GCP uses IAM bindings with members and roles? The key is not to memorize every schema but to master a prompting strategy that abstracts these differences, allowing you to focus on intent while the AI handles the provider-specific implementation.

Azure Resource Manager (ARM) / Bicep Strategies

Working within the Azure ecosystem means embracing its declarative tooling, either ARM JSON or the more user-friendly Bicep. A common pitfall is forgetting Azure’s strict naming conventions and dependency chains. For example, a Virtual Network cannot exist without a Resource Group, and a subnet is a child resource of the VNet. Your prompts must reflect this hierarchy to generate valid code.

Instead of a generic request, provide the full context of your Azure environment. This approach leverages the AI’s knowledge of Azure’s resource provider (e.g., Microsoft.Network/virtualNetworks) and required properties like addressSpace.

• Prompt for a foundational Resource Group and VNet:

  // Generate a Bicep file for a new Azure environment.
  // 1. Create a Resource Group named 'rg-prod-app-001' in the 'East US' location.
  // 2. Within that resource group, create a VNet named 'vnet-hub-prod' with the address space '10.0.0.0/16'.
  // 3. Add a subnet named 'snet-app' with the address prefix '10.0.1.0/24'.
  // 4. Apply standard tags: Environment=Prod, Project=App.

  This prompt works because it explicitly defines the resource hierarchy and uses Azure-centric names (rg, vnet, snet), guiding the AI to generate a Bicep file with proper resource declarations and symbolic names for dependencies.

Google Cloud Platform (GCP) Configurations

GCP’s approach, particularly with its IAM model, requires a different prompting mindset. While Azure often uses role assignments tied to a principal, GCP frequently uses an IAMPolicy or IAMBinding resource that attaches directly to the resource itself (like a project or a storage bucket). The AI needs to understand this distinction to generate correct Terraform or Deployment Manager code.

When prompting for GCP, focus on the resource’s lifecycle and its specific IAM structure. For instance, a Cloud Storage bucket’s IAM policy is a separate resource that binds to the bucket’s self-link.

• Prompt for a secure GCP Storage Bucket with specific IAM roles:

  // Create a Terraform configuration for a Google Storage Bucket.
  // The bucket name should be unique, like 'app-data-lake-<RANDOM_ID>'.
  // It must be in the 'us-central1' region and use Uniform Bucket-Level Access.
  // Grant the 'roles/storage.objectViewer' role to the group '[email protected]'.
  // Ensure the bucket enforces KMS encryption using a key from a pre-existing KMS key ring.

  This prompt succeeds by specifying the exact IAM role (roles/storage.objectViewer) and the GCP-specific identity format ([email protected]). It also calls out critical security features like Uniform Bucket-Level Access and CMEK, which are non-negotiable for enterprise-grade configurations.

Abstracting Cloud Differences: The Universal Prompting Framework

The most powerful technique for multi-cloud environments is to stop thinking in terms of cloud-specific resources and start thinking in terms of architectural intent. This “Universal Prompting Framework” allows you to write a single, high-level prompt and let the AI’s model, which has been trained on the schemas for all major providers, generate the correct implementation for your target environment.

The core principle is to describe the what and the why, not the how. You define the logical components and their relationships, and you instruct the AI on which provider’s syntax to use.

• Universal Prompt Example:

  // I need a secure, three-tier application network layout.
  // - A public-facing load balancer.
  // - A web tier of 2 VMs behind the load balancer in a private subnet.
  // - A database tier in a separate, isolated private subnet.
  // Generate the Terraform code for an AWS deployment.

  Now, change only the provider:

  // ... [same description of the three-tier layout] ...
  // Generate the Bicep code for an Azure deployment.

  You’ll get an aws_lb and aws_instance in the first case, and an Microsoft.Network/loadBalancers and Microsoft.Compute/virtualMachines in the second. This method is incredibly efficient for rapid prototyping and ensuring architectural consistency across clouds.

Cross-Cloud Security Standards

Security is the one area where you cannot afford inconsistencies. A “compliant storage bucket” has the same core requirements regardless of the provider: encryption at rest, restricted public access, and logging enabled. You can use the Universal Prompting Framework to enforce these standards universally.

By framing your prompt around a security policy rather than a resource name, you force the AI to apply the correct provider-specific settings to meet the policy’s intent. This is far more effective than trying to remember the exact property for disabling public ACLs on S3 versus GCS.

Golden Nugget: When generating security configurations, always ask the AI to produce the code and a brief justification for each security setting it applied. For example: “Generate a compliant storage bucket on Azure and GCP that enforces encryption at rest and private access. After the code, list each security-related property and explain why it’s necessary.” This not only gives you the code but also serves as a learning tool and an audit trail, reinforcing your team’s security knowledge.

This approach ensures that whether you’re deploying to AWS, Azure, or GCP, your baseline security posture remains locked down. The AI translates your high-level security intent—“private, encrypted, and audited”—into the precise, low-level configurations that make it happen.

Advanced IaC Patterns: Modules, State, and Loops

You’ve mastered the basics of generating individual cloud resources, but what happens when your infrastructure grows beyond a single EC2 instance or S3 bucket? The real challenge in Infrastructure as Code isn’t creating resources—it’s orchestrating them at scale. This is where many teams stumble, leading to duplicated code, fragile configurations, and state file conflicts. How do you evolve from writing disposable scripts to building a robust, reusable, and collaborative infrastructure platform?

This section tackles the three pillars of advanced IaC: abstraction through modules, dynamic configuration with loops, and collaboration via state management. We’ll leverage Cursor’s AI to automate the complex boilerplate, allowing you to focus on architecture. By the end, you’ll be able to prompt the AI to refactor monolithic code into elegant modules, generate dynamic configurations that adapt to your environment, and implement bulletproof state management from day one.

Generating Reusable Modules: From Monolith to Microservices

The single most effective way to scale your IaC is by creating reusable modules. Instead of copying and pasting resource definitions for each application environment, you define a blueprint once and instantiate it multiple times. This enforces consistency and dramatically reduces maintenance overhead. However, abstracting resources into a module—especially defining clean input variables and outputs—can be tedious.

Cursor’s AI excels at this structural refactoring. You can provide a block of existing, hard-coded resource definitions and ask the AI to perform the abstraction for you.

Actionable Prompt Example:

“Refactor the following Terraform code into a reusable module named ‘web-server’. Create a main.tf file inside the module that defines an aws_instance and an aws_security_group. Generate a variables.tf file to parameterize inputs like instance_type, ami_id, and ingress_ports. Finally, create an outputs.tf file to expose the instance’s private IP and security group ID.”

The AI will not only move the resources into the correct files but will also intelligently create the variable and output blocks. It understands that a hard-coded ami = "ami-0c55b159cbfafe1f0" inside the module is bad practice, so it will replace it with ami = var.ami_id. This transforms a fragile, one-off script into a version-controlled, shareable component that your entire team can consume.

Golden Nugget: When prompting for module generation, always specify the desired output. A common mistake is creating a module that provisions resources but fails to expose critical information like ARNs or IDs. Explicitly asking for outputs in your prompt ensures the module is truly composable and can be linked to other parts of your infrastructure.

Handling Loops and Dynamic Blocks: Taming Configuration Sprawl

The hardest part of IaC is often managing repetitive but slightly different configurations. Think about security group rules: you might need to open ports 80, 443, and 22, but only for your production environment. Or perhaps you need to create an IAM policy with a dynamic list of S3 bucket ARNs. Hard-coding these creates maintenance nightmares. The solution is loops and dynamic blocks, but the syntax can be unintuitive.

This is a perfect use case for AI-assisted development. Instead of wrestling with for_each, count, and dynamic blocks, you can describe the logic in plain English.

Actionable Prompt Example:

“I have a list of ingress ports [80, 443, 22] stored in a Terraform variable called web_ports. Generate a dynamic "ingress" block within my aws_security_group resource to create a rule for each port in the list. The rules should all use tcp protocol and allow traffic from 0.0.0.0/0.”

For more complex scenarios, like creating multiple AWS instances based on a map variable, the prompt is just as straightforward:

“Given a Terraform variable servers = { web = 't3.micro', api = 't3.small' }, write a for_each loop for the aws_instance resource. The key should be used for the Name tag, and the value should be the instance_type.”

The AI will generate the correct for_each = var.servers syntax and correctly reference the key and value within the resource using each.key and each.value. This approach lets you define your configuration data in a simple structure (like a .tfvars file) and lets the AI handle the complex plumbing.

State Management and Remote Backends: The Foundation of Teamwork

A local terraform.tfstate file is a ticking time bomb in any team environment. If two developers run terraform apply simultaneously, the last one wins, potentially causing conflicts and orphaned resources. The professional standard is to use a remote backend, like Amazon S3 with DynamoDB for state locking, to ensure that state is centralized, versioned, and safely locked during operations.

Configuring this backend correctly is critical, and a single misstep can leave your state unprotected. You can use AI to generate the exact code needed for both the bootstrap resources and the backend configuration.

Actionable Prompt Example:

“Generate two separate Terraform configurations. First, create the ‘bootstrap’ code to provision an S3 bucket for state storage and a DynamoDB table for state locking. The S3 bucket must have versioning and server-side encryption enabled. Second, generate the terraform { backend "s3" ... } block that an application’s main.tf would use to connect to that newly created bucket and table.”

This prompt is powerful because it asks for two distinct but related pieces of code. The AI will provide the aws_s3_bucket and aws_dynamodb_table resources for setup, and then the specific terraform { backend "s3" ... } block for the application code. This ensures your team has a clear, repeatable process for enabling safe, collaborative IaC management from day one.

Testing and Validation: Shifting IaC Quality Left

In 2025, you can’t afford to discover syntax errors or misconfigurations during a terraform apply in production. Shifting “left” means validating your code before it ever reaches an execution environment. While full-blown unit testing frameworks exist, you can achieve significant quality gains with simple AI-generated validation scripts.

Actionable Prompt Example:

“Write a simple Bash script that automates Terraform validation and planning. The script should first run terraform init, then terraform validate to catch syntax errors. If validation passes, it should run terraform plan -out=tfplan and echo a success message. If any step fails, the script should exit with a non-zero status code and print an error.”

This prompt generates a robust CI/CD-ready script you can run locally or in a pipeline. For more advanced testing, you can ask the AI to generate basic unit tests using a framework like terratest.

Actionable Prompt Example (Advanced):

“Using Go and the terratest library, write a basic unit test for an S3 bucket Terraform module. The test should deploy the module, check that the bucket ARN is not null, and then destroy the infrastructure.”

By integrating these AI-generated validation and testing steps, you build a safety net that catches errors early, ensuring your infrastructure code is not just functional but reliable.

Real-World Application: Building a Scalable Web App Stack

What does it actually look like to move from simple resource prompts to orchestrating a full, production-grade architecture? Let’s walk through a common scenario: deploying a scalable, auto-scaling web application on AWS using Terraform. Instead of manually writing hundreds of lines of code, we’ll use a conversational, step-by-step prompting workflow in Cursor to build our stack piece by piece. This approach ensures each component is correctly configured and integrated with the others.

The Scenario: Deploying a Scalable Web App on AWS

Our goal is to build a resilient infrastructure that can handle fluctuating traffic. The core components will be a Virtual Private Cloud (VPC) for network isolation, an Application Load Balancer (ALB) to distribute traffic, an Auto Scaling Group (ASG) to manage our web servers, and a managed RDS database for persistent data storage. The key here is that we won’t just ask for the whole stack at once. We’ll build it layer by layer, which allows the AI to maintain context and correctly reference resources created in previous steps.

Step-by-Step Prompting Workflow

We start with the foundation—the network. A well-architected network is non-negotiable for a secure and scalable application.

1. Prompting for the VPC and Networking: Our first prompt is specific, defining not just the VPC but the required public and private subnets across two availability zones for high availability.

Prompt: “Generate a Terraform configuration for a production-grade AWS VPC. It should have a CIDR block of 10.0.0.0/16. Create two public and two private subnets, each in us-east-1a and us-east-1b. Ensure the public subnets have a route to an Internet Gateway, and the private subnets have a route through a NAT Gateway. Tag all resources with the project name ‘scalable-app’.”

This prompt gives the AI the architectural blueprint. It will generate the aws_vpc, aws_subnet, aws_internet_gateway, aws_nat_gateway, and associated route tables. By specifying the AZs and routing, you prevent common misconfigurations that lead to instances being unable to reach the internet.

2. Prompting for the Application Load Balancer: Next, we need the ALB to sit in our public subnets and route traffic to our future web servers.

Prompt: “Building on the previous VPC configuration, create an AWS Application Load Balancer. It should be placed in the two public subnets. Create a security group for the ALB that allows inbound traffic on port 80 from any IP. Define a target group and a listener on port 80 that forwards traffic to this target group. The target group should health check the / path.”

The AI now understands the context. It will correctly reference the aws_vpc resource and the IDs of the public subnets generated in step one. It will also create the necessary security groups, ensuring the ALB is the only entry point for web traffic. This chaining of prompts is where the real time savings happen.

3. Prompting for the Auto Scaling Group and Launch Template: This is the heart of our compute layer. We need to define what our servers are and how many to run.

Prompt: “Create an AWS Launch Template and Auto Scaling Group. The instance type should be t3.micro. Use the latest Amazon Linux 2 AMI. The instances must be launched into the private subnets. Configure the ASG to attach these instances to the ALB target group created earlier. Set the desired capacity to 2, minimum to 2, and maximum to 4. The instances need a security group that allows inbound traffic on port 80 only from the ALB’s security group.”

This is a critical prompt. By specifying “only from the ALB’s security group,” we demonstrate the principle of least privilege. The AI will generate the aws_launch_template and aws_autoscaling_group resources, and it will correctly interpolate the security group IDs, creating the tight, secure integration we need.

4. Prompting for the RDS Instance: Finally, the database. It needs to be in a private subnet and accessible only by our application servers.

Prompt: “Add a Terraform configuration for an AWS RDS instance. Use the db.t3.micro class with MySQL engine. Place it in the two private subnets we defined earlier. Create a database security group that allows inbound traffic on port 3306, but only from the security group of our web servers (the one used in the Launch Template).”

Again, the AI leverages context. It knows about the private subnets and the web server security group from previous steps, wiring everything together correctly.

Debugging with AI: The Iterative Loop

It’s rare that the first terraform plan runs perfectly. Let’s say you apply this configuration and get an error: Error: creating Auto Scaling Group: InvalidQueryParameter: Invalid IAM Instance Profile ARN. You didn’t even ask for an IAM profile. Instead of spending an hour searching forums, you use Cursor’s chat.

You (pasting the error): “I got this error when applying the ASG. It seems to need an IAM instance profile. Can you generate the necessary IAM role and instance profile resource, and then update the Launch Template to use it?”

The AI instantly understands the gap. It will generate the aws_iam_role, aws_iam_instance_profile, and the necessary aws_iam_policy_attachment for the EC2 service, and then modify the aws_launch_template to include the iam_instance_profile block. This turns a potential half-day debugging session into a two-minute fix.

Refactoring for Cost Optimization: A “Before and After”

The initial prompt gave us a solid, functional setup. But it used On-Demand t3.micro instances. What if we could save up to 90% on compute costs by using Spot Instances for fault-tolerant workloads like web servers? We can ask the AI to refactor.

Before (Initial Launch Template):

resource "aws_launch_template" "web_lt" {
  image_id      = data.aws_ami.amazon_linux_2.id
  instance_type = "t3.micro"
  # ... other configs
}

The Optimization Prompt:

Prompt: “Refactor this Terraform Auto Scaling Group and Launch Template to use a mix of EC2 Spot Instances for cost savings. Configure it to use t3.micro and t3.small as the instance types and set the spot_allocation_strategy to ‘capacity-optimized’ so AWS picks the best available instance type. Ensure the ASG has a mixed instances policy.”

After (Optimized Configuration): The AI will replace the simple instance_type with a mixed_instances_policy block, defining the instance types and the spot strategy. This single prompt can lead to significant, recurring monthly savings without sacrificing the application’s scalability. It’s a powerful example of how AI can act as a cost-optimization partner, not just a code generator.

Conclusion: The Future of Infrastructure Development

The true power of integrating AI prompts into your Infrastructure as Code workflow isn’t just about writing code faster; it’s about fundamentally changing how you think about building cloud systems. By letting the AI handle the tedious schema lookups and syntax minutiae, you reclaim the mental bandwidth to focus on what truly matters: designing a resilient, scalable, and secure architecture. The hours saved from cross-referencing API documentation translate directly into more time for strategic planning and optimization.

From Syntax Writer to System Architect

This shift marks a pivotal change in the developer’s role. You are no longer a “syntax writer” meticulously crafting resource blocks line by line. Instead, you evolve into a system architect and validator. Your primary value is now in defining the high-level intent—security boundaries, data flow, and cost-efficiency—and then expertly guiding the AI to produce the correct implementation. The prompt becomes the new blueprint, and your expertise is the critical judgment that ensures the final structure is sound.

To master this new paradigm, start small. Begin with single, well-defined resources like an S3 bucket or a virtual network. As you gain confidence, build a personal or team prompt library tailored to your organization’s specific naming conventions, security policies, and architectural patterns. This is the golden nugget: the most effective prompts aren’t generic; they are infused with your unique organizational context, transforming a general-purpose tool into a bespoke co-pilot for your specific environment.

Critical Warning

Frequently Asked Questions

Q: Why is context so critical in IaC prompts

Context allows the AI to understand the environment and dependencies, preventing generic code that lacks necessary security groups, networking, or specific provider configurations

Q: Should I generate large IaC files at once

No, use iterative refinement. Start with a skeleton structure and refine specific resource blocks layer by layer to maintain control and readability

Q: What is the main bottleneck in traditional IaC

The constant context switching between writing code and verifying schema details in API documentation, which breaks focus and introduces errors