Prompt InFlow

DevOps Prompts

Dockerfiles, CI pipelines and IaC scaffolds.

3+ prompts
4 AI models
Advanced
DevOps prompts
7
AI models supported
3
Parent category
Coding
Total library
410+

DevOps prompts help you produce devops faster using ChatGPT, Claude, Gemini and more. Browse optimized prompts for coding workflows — copy, tweak the placeholders, and ship.

Editorial

The devops prompt library, explained

This is the DevOps hub, part of our broader Coding collection. You'll find 7 devops prompts written specifically for this workflow — each one structured to give the AI the role, audience, format and constraints it needs to produce output you can use immediately.

These prompts are built for software engineers, ML engineers and indie devs who want code, tests and architecture suggestions they can ship. They work across the major AI models — ChatGPT, Claude, Gemini and the leading image and video generators — and they hold up whether you're prompting in a chat window, an editor extension or a workflow tool. Pull these in during scoping, scaffolding, refactors and code reviews — anywhere a teammate would help.

Why these prompts work: each one starts with a clear role, names the audience or scene, defines the output format and adds 2–3 hard constraints. That structure is what separates a vague AI answer from a usable one. The library is curated so you don't have to test 30 versions of the same prompt to find the one that actually performs — that work is already done.

Expect production-style code, types, edge-case handling and at least a smoke test — written in the style you specify. You'll occasionally want to regenerate or refine, especially the first time you use a prompt with your own context. Treat each prompt as a starting structure: the variables and constraints stay, the topic and tone become yours.

Read each prompt before you copy it. Swap the placeholders for your real audience, product and constraints, and add one or two engineering-specific details from your own brief. The prompts here are designed to be edited — the more context you bring, the stronger the output.

If this is your first time using AI for devops, start with the featured prompt below and adapt it to your context. Then explore the related use cases for adjacent workflows that often pair well with this one.

⭐ Editor's Choice

The featured prompt on this page

One prompt we'd ship today. Read why it works, see a preview, and copy it in a click.

Editor's Choice · Tuned for Claude

Build a Docker Compose Local Development Stack

Why it works · It defines the runtime, behaviour and edge cases, then asks for tests — turning the AI from a code generator into a reliable engineering pair.

Best use case

Best for devops workflows where generate a full docker compose configuration for a local development environment, integrating your application, postgresql, redis, and mailhog. it includes named volumes for data persistence, a `wait-for-db` entrypoint for service reliability, and provides essential guidance on image size and security for a robust setup.

Expected output

Working code in the runtime you specified, with edge cases handled and a small test you can run immediately.

Open full prompt

Generate a full Docker Compose configuration for a local development environment, integrating your application, PostgreSQL, Redis, and MailHog. It includes named volumes for data persistence, a `wait-for-db` entrypoint for service reliability, and provides essential guidance on image size and security for a robust setup.

Featured Prompts

Top DevOps prompts

Prompts to copy, tweak and ship right now.

Coding
Claude

Build a Docker Compose Local Development Stack

Generate a full Docker Compose configuration for a local development environment, integrating your application, PostgreSQL, Redis, and MailHog. It includes named volumes for data persistence, a `wait-for-db` entrypoint for service reliability, and provides essential guidance on image size and security for a robust setup.

View prompt →
Coding
Claude

Kubernetes Production Manifests for Stateless APIs

Generate comprehensive Kubernetes YAML manifests for a stateless API, including Deployment, HPA, PDB, and Service. Platform engineers can create production-grade configurations for EKS/GKE, ensuring resilience and scalability through GitOps-friendly output.

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Coding
Claude

Optimize Docker Images: Reduce 1.2GB to under 150MB for CI/CD

This guide helps platform engineers optimize Docker images, shrinking them from 1.2GB to under 150MB. It provides a detailed process to achieve significant size reduction and enhance security, all while ensuring application runtime stability for efficient CI/CD pipelines.

View prompt →
Coding
Claude

Kubernetes NetworkPolicy for Zero-Trust with Observability

This prompt guides platform and security engineers in generating Kubernetes NetworkPolicy manifests. It ensures a zero-trust security posture across multiple namespaces while explicitly preserving crucial observability traffic. The output includes K8s YAML, Helm/Kustomize guidance, and a deployment strategy.

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Coding
Claude

Kubernetes CrashLoopBackOff: Diagnosing Scale-Dependent Failures

SREs can use this guide to debug Kubernetes pods encountering CrashLoopBackOff issues that only appear at high replica counts. It provides structured kubectl steps and manifest analysis to identify resource contention or race conditions emerging under load, ensuring application resilience and stability.

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Coding
Claude

Secure, Compact Multi-Stage Docker for Node.js APIs

Craft a secure, lightweight multi-stage Dockerfile for Node.js APIs, targeting sub-150MB images on a distroless base. This solution provides a Dockerfile, Docker Compose setup, and essential notes on build, runtime, security, and image size optimization, helping engineers ship production-ready services efficiently.

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Coding
Claude

Optimize Node.js 20 pnpm Dockerfiles for Production

Generate a multi-stage Dockerfile for Node.js 20 applications using pnpm, focusing on dependency caching and a compact runtime image. This ensures faster builds and smaller deployment artifacts, critical for production environments. Includes Docker Compose and build argument guidance.

View prompt →

Model comparison

Best AI models for devops

A ranked shortlist of the models PromptInFlow users reach for first.

  1. #1

    ChatGPT

    General-purpose reasoning

    7 devops prompts

  2. #2

    Gemini

    Multimodal reasoning

    7 devops prompts

  3. #3

    Claude

    Long-form writing & analysis

    7 devops prompts

Prompt Writing Guide

How to write better DevOps prompts

Six habits that consistently produce stronger AI output. Apply them to any prompt on this page.

  1. 1

    State the runtime and stack

    Language, version, framework, target platform. "Node 20 on Cloudflare Workers" produces very different code than "any JavaScript".

  2. 2

    Write the requirements first

    Functional behaviour, inputs, outputs, error handling. The AI is excellent at following a spec and mediocre at guessing intent.

  3. 3

    List the edge cases

    Empty arrays, null, network failures, race conditions, oversize payloads. Naming them up front prevents the "happy path only" trap.

  4. 4

    Ask for tests

    Request a small test suite alongside the implementation. Tests force the model to reason about boundaries — and you get verification for free.

  5. 5

    Pin code style

    TypeScript strict, no `any`, named exports, early returns. Pin style and you'll spend zero time on lint cleanup.

  6. 6

    Demand explanations on tricky parts

    Ask the model to comment any non-obvious step. Comments expose flawed reasoning before you ship the code.

Best Practices

DevOps best practices

Apply these in every prompt on this page. Small habits, outsized improvements in output quality.

Pin the runtime and version

Language + framework + version. Skip and you'll get half-deprecated APIs.

Write the spec first

Inputs, outputs, errors, edge cases. The AI is a great spec follower.

Ask for tests alongside code

Tests catch flawed reasoning before you ship.

Forbid `any` in TypeScript

One rule removes 80% of weak AI-generated TS code.

Avoid These

Common DevOps prompt mistakes

The same handful of mistakes are responsible for most weak AI output. Catch them before you hit send.

  • Missing the runtime

    Different runtimes have different APIs. Without it, the AI guesses — usually wrongly.

  • Glossing over edge cases

    If the prompt only describes the happy path, the code will only handle the happy path.

  • Asking for "clean code"

    "Clean" is subjective. Specify naming, style and lint rules instead.

  • Skipping tests

    Without tests you have no way to verify the AI's reasoning. Always ask for at least a smoke test.

  • Pasting too much code

    Dump only the relevant functions and types. Long context dilutes attention.

Pro Tips

Advanced prompt patterns

Side-by-side rewrites that show what separates a weak prompt from a great one.

Instead of

“Write a function to debounce.”

Use

“Write a TypeScript `debounce<T>` higher-order function targeting Node 20. Cancel in-flight calls, return a `.cancel()` method, preserve `this`, type the return as `(...args: Parameters<T>) => void`. Include 4 Vitest tests covering rapid calls, cancel, late call, and `this` binding.”

Why it works: Stack, behaviour, types and tests are all defined.

Instead of

“Make this API.”

Use

“Implement a REST endpoint POST /api/invoices in Hono on Cloudflare Workers. Validate body with Zod. Return 400 on invalid input, 409 on duplicate, 201 with the new invoice on success. Include a small handler test and a sample curl.”

Why it works: Framework, runtime, errors and verification are pinned.

Instead of

“Fix this bug.”

Use

“Here's the error: `TypeError: cannot read 'map' of undefined`. The function receives `items` from the API. Add a guard, log a warning when it's missing, and update the test to cover the empty case. Don't change the public API.”

Why it works: Symptom, expected fix and scope are explicit.

Instead of

“Optimise this query.”

Use

“This Postgres query takes 1.4s on a 12M-row `events` table. The filter is `user_id = ? AND created_at > ?`. Propose 2 index strategies, explain the trade-offs, and rewrite the query if helpful.”

Why it works: Volume, filter and trade-off framing produce useful answers.

Instead of

“Refactor this code.”

Use

“Refactor this React component to extract the data fetching into a TanStack Query hook. Keep the rendering identical. Add types, no `any`, and update tests if they reference the old API.”

Why it works: Boundary and constraints prevent over-refactoring.

Browse by Model

Every AI model for devops

Jump into the model hub to see how each one handles this workflow.

ChatGPT

General-purpose reasoning

Claude

Long-form writing & analysis

Gemini

Multimodal reasoning

Grok

Realtime, witty answers

Related

Related use cases

More from Coding.

Code Review

Senior-engineer-grade reviews of diffs and PRs.

Debugging

Stack trace analysis and root-cause hunts.

Refactoring

Safe, incremental refactors with tests.

Architecture

System design and trade-off analysis.

Parent category

Browse all Coding prompts

DevOps is one of 5 coding workflows on PromptInFlow. Open the full hub for the complete library, FAQs and supported AI models.

Visit Coding hub

Collections

Prompt collections

Curated groups for different skill levels and goals.

6 prompts

Beginner Prompts

Easy starters with clear placeholders — copy, paste, done.

5 prompts

Advanced Prompts

Multi-step prompts with role, constraints and structured output.

4 prompts

Marketing Prompts

Conversion-focused angles built on proven frameworks.

5 prompts

Professional Prompts

Polished, client-ready outputs for day-to-day work.

FAQ

DevOps prompts, answered

Common questions about these prompts.

Related searches

People also search for devops prompts

Popular searches related to this page. Each one links to the closest matching prompt library.

Explore more DevOps prompts

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