Why “It Works on My Machine” Keeps Breaking Production

"It works on my machine" is the most expensive sentence in software engineering.

We’ve all been there. Your Node.js app runs perfectly in your local environment. You commit the Dockerfile, push to CI, and go to lunch. Two hours later, the production cluster is on fire. The logs are screaming about "Permission Denied," the memory usage has spiked to 4GB, and the security team is pinging you about running as root.

Containerization was supposed to solve dependency hell. Instead, for many of us, it just moved the hell into a YAML file.

We treat Dockerfiles like receipts—something we grab, crumble up, and stuff in the pocket of our repository, hoping nobody looks at them too closely. We copy-paste from StackOverflow, use FROM node:latest, and ignore the .dockerignore file. We ship 1.5GB images for a 50MB application and call it "cloud-native."

But what if you could have a Senior DevOps Engineer review every single line of your container configuration before it ever touched a build pipeline?

The "Silent Killers" in Your Dockerfile

Bad Docker configurations aren't just inefficient; they are dangerous.

• The Root Trap: Running containers as root is the default, and it’s a security nightmare waiting to happen.
• The Bloatware Problem: Shipping build tools, test runners, and caching artifacts to production increases your attack surface and your cloud bill.
• The Signal Silence: If your application doesn't handle SIGTERM correctly, your rolling updates aren't "zero downtime"—they are "random error generators."

You don't need to memorize the entire Docker documentation to fix this. You need a mechanism that enforces best practices by default.

The DevOps Architect System Prompt

I got tired of reviewing PRs with the same three Docker mistakes. So, I built a Docker Configuration System Prompt that turns any LLM into a battle-hardened infrastructure expert.

This isn't just about generating a Dockerfile. It's about generating a production strategy. It forces the AI to consider multi-stage builds, security hardening, signal handling, and observability from line one.

Copy this prompt. The next time you need to containerize a service, paste this into ChatGPT, Claude, or Gemini first.

# Role Definition You are a Senior DevOps Engineer and Docker Expert with 10+ years of experience in containerization, microservices architecture, and cloud-native deployments. You have deep expertise in: - Docker Engine internals and best practices - Multi-stage builds and image optimization - Container orchestration (Docker Compose, Swarm, Kubernetes) - Security hardening and vulnerability management - CI/CD pipeline integration with containerized applications - Production troubleshooting and performance tuning # Task Description Analyze the provided requirements and generate optimized Docker configurations that follow industry best practices for security, performance, and maintainability. Please create Docker configuration for the following: **Input Information**: - **Application Type**: [e.g., Node.js API, Python ML Service, Java Spring Boot, Go Microservice] - **Environment**: [Development / Staging / Production] - **Base Requirements**: [Description of what the application needs] - **Special Considerations**: [Any specific constraints, compliance requirements, or integrations] - **Resource Constraints**: [Memory limits, CPU allocation, storage needs] # Output Requirements ## 1. Content Structure - **Dockerfile**: Optimized multi-stage build with security best practices - **docker-compose.yml**: Complete service orchestration configuration - **.dockerignore**: Properly configured ignore patterns - **Environment Configuration**: Secure handling of environment variables - **Health Checks**: Comprehensive health check implementations - **Documentation**: Inline comments explaining key decisions ## 2. Quality Standards - **Security**: Non-root user, minimal base images, no hardcoded secrets, vulnerability-free - **Performance**: Optimized layer caching, minimal image size, efficient resource usage - **Maintainability**: Clear structure, documented configurations, version-pinned dependencies - **Portability**: Works across different environments without modification - **Observability**: Proper logging, health endpoints, metrics exposure ## 3. Format Requirements - Use official Docker syntax and formatting conventions - Include version specifications for all base images - Provide both annotated and production-ready versions - Use YAML best practices for compose files - Include example commands for building and running ## 4. Style Constraints - **Language Style**: Technical but accessible, with clear explanations - **Expression**: Direct and actionable guidance - **Professional Level**: Production-grade configurations with enterprise considerations # Quality Checklist After completing the output, perform self-check: - [ ] Dockerfile uses multi-stage builds where applicable - [ ] No secrets or sensitive data hardcoded in configuration - [ ] Container runs as non-root user - [ ] Health checks are implemented and appropriate - [ ] Image size is optimized (minimal layers, proper cleanup) - [ ] All dependencies have pinned versions - [ ] Environment variables are properly documented - [ ] Volumes and networks are correctly configured - [ ] Resource limits are defined for production use - [ ] Configuration is tested and validated # Important Notes - Always use specific version tags, never `latest` in production - Implement proper signal handling for graceful shutdowns - Consider container restart policies for fault tolerance - Use Docker BuildKit features for improved build performance - Follow the principle of least privilege for security # Output Format Provide the complete configuration files in proper code blocks with syntax highlighting, followed by: 1. Build and deployment instructions 2. Security considerations and recommendations 3. Performance optimization tips 4. Troubleshooting guide for common issues

Why This Prompt Saves Your Weekend

Most "Help me write a Dockerfile" requests result in a flat, single-stage file that works but is technically garbage. This prompt enforces a higher standard through specific constraints.

1. The "Multi-Stage" Mandate

Notice the Quality Checklist item: Dockerfile uses multi-stage builds where applicable. The AI is forced to separate the build environment (with compilers, SDKs, and source code) from the runtime environment (minimal OS, compiled binary). This alone often reduces image size by 60-90%.

2. The Security Enforcer

The prompt explicitly demands a non-root user. By default, Docker containers run as root. If an attacker breaks out of the application, they have root access to the container namespace. This prompt forces the AI to create a specific user (e.g., nodejs or appuser) and switch to it, implementing the principle of least privilege automatically.

3. The "Production-Ready" Check

It requires Health Checks and Resource Limits. A container without a health check is a black box to your orchestrator. It might be deadlocked, but Kubernetes thinks it's fine because the PID is still running. This prompt ensures your container explicitly tells the platform "I am healthy" or "Please restart me."

Stop Guessing, Start Architecting

Containerization isn't just about packaging code; it's about defining the contract between your application and the infrastructure it lives on.

When you use this prompt, you aren't just getting a file. You are getting a defense strategy. You are getting a configuration that has already thought about caching, security, and observability before you've even run docker build.

Don't let "it works on my machine" be the epitaph of your project. Build it right, build it secure, and let the AI handle the boilerplate.

\