The DevOps Hack That Lets PMs Experiment with Prompts (Safely)

In fast-moving AI applications, prompt iteration is critical—but constantly rebuilding and deploying your app just to tweak prompts can be a major bottleneck.

This post introduces a scalable pattern for separating prompts from code using AWS Parameter Store, Infrastructure as Code (IaC) tools like Terraform or CloudFormation, and CI/CD automation. The result? Non-technical team members can safely experiment with prompts—without engineering intervention.

Why Decouple Prompts from Code?

Prompt tuning is an iterative, often creative process. Tight coupling of prompts to application code slows everyone down. By treating prompts as externally managed configuration:

• You eliminate the need for full redeploys

• You enable fast, safe iteration

• You let non-dev stakeholders contribute meaningfully

• This also help improve auditability, versioning, and rollback safety

Instead of hardcoding prompts into your app, store them in an external key-value store like AWS Systems Manager Parameter Store or Secrets Manager. Each prompt becomes a versioned, externally accessible configuration parameter.

This way, your app simply reads the latest prompt on initialization or via scheduled refresh—no redeploy necessary.

How to set it up

Example implementation

Here's a sample setup using GitHub Actions, AWS CloudFormation, and AWS Systems Manager Parameter Store to manage and deploy prompts. While this example uses these specific tools, the underlying pattern is flexible and can be adapted to any IaC (Infrastructure as Code) framework, CI/CD pipeline, and external key-value store—such as Terraform, GitLab CI, or even Feature Flags and Secrets Manager.

Folder structure

prompt-store/
├── cf-template.yaml
├── .github/
│   └── workflows/
│       └── deploy-prompts.yml
└── README.md

Sample Cloudformation template

AWSTemplateFormatVersion: "2010-09-09"
Description: CloudFormation template to manage prompts
Parameters:
  Environment:
    Type: String
    AllowedValues:
    - dev
    - prod
Resources:
  Agent1Prompt:
    Type: AWS::SSM::Parameter
    Name: !Sub "/myapp/${Environment}/agent1"
    Type: String
    Tier: Intelligent-Tiering
    Description: Prompt for Agent 1

  Agent2Prompt:
    Type: AWS::SSM::Parameter
    Name: !Sub "/myapp/${Environment}/agent2"
    Type: String
    Tier: Intelligent-Tiering
    Description: Prompt for Agent 2

Github Action Configuration

name: Update Prompts
permissions:
    id-token: write # This is required for requesting the JWT
    contents: read  # This is necessary for actions/checkout
on:
    push:
        branches:
        - main
        paths:
        - cf-template.yaml

    pull_request_target:
        branches:
        - main
        types: [labeled]
        paths:
        - cf-template.yaml

    pull_request:
        branches:
        - main
        paths:
        - cf-template.yaml
jobs:
    update_prompts:
      name: Update Prompts
      runs-on: ubuntu-latest
      steps:
        - name: Checkout
          uses: actions/checkout@v4
        - name: Configure AWS credentials
          uses: aws-actions/configure-aws-credentials@v4
          with:
            role-to-assume: "arn:aws:iam::$ACCOUNT_ID:role/github-actions"
            aws-region: "us-east-1"

        - name: Set up Python
          uses: actions/setup-python@v4
          with:
            python-version: "3.10"

        - name: Install dependencies
          run: |
            pip install awscli
        - name: deploy-prompts
          run: |
            if [[ ${{ github.ref_name }} == "main" ]]; then
              echo "Current branch is main"
              aws cloudformation deploy --template-file cf-template.yaml \
                --stack-name prod-agent-parameters \
                --parameter-overrides Environment=prod \
                --no-fail-on-empty-changeset
            else
              echo "Current branch is not main"
              aws cloudformation deploy --template-file cf-template.yaml \
                --stack-name dev-agent-parameters \
                --parameter-overrides Environment=dev \
                --no-fail-on-empty-changeset
            fi

Wrapping up

Decoupling AI prompts from your core application code isn't just a technical optimization—it's a collaboration unlock. By using tools like AWS Parameter Store, CloudFormation, and GitHub Actions, you enable non-technical stakeholders to safely contribute to the intelligence of your AI systems, while still maintaining the guardrails and visibility engineering teams need.

This pattern transforms prompt engineering into a team sport—letting PMs, marketers, and CX leaders experiment and iterate quickly, without compromising security or reliability.

Whether you're building onboarding flows, support bots, or internal copilots, this approach gives you:

• Speed - Faster prompt updates and testing

• Control: Enables versioning, validation, auditability and roll-back ability

• Collaboration: Empower teams across various job functions

• Agility: A foundation for real-time, data-driven prompt tuning