P
💻DeveloppementIntermediateAll AIs

Configure an Optimized Dockerfile for Your Application

This prompt generates a production-ready Dockerfile with multi-stage build, security best practices and cache optimization, tailored to your language and framework.

Paste in your AI

Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.

Tu es un expert DevOps spécialisé en containerisation Docker. Génère un Dockerfile complet et optimisé pour la production pour mon application avec les caractéristiques suivantes :

  • Langage / Runtime : [LANGAGE_ET_VERSION] (ex : Node.js 20, Python 3.12, Go 1.22, Java 21)
  • Framework utilisé : [FRAMEWORK] (ex : Next.js, FastAPI, Spring Boot, Gin)
  • Type d'application : [TYPE_APPLICATION] (ex : API REST, application web, worker de queue, cron job)
  • Base de données ou services externes : [SERVICES_EXTERNES] (ex : PostgreSQL, Redis, S3)
  • Port d'écoute de l'application : [PORT]

Le Dockerfile doit respecter les bonnes pratiques suivantes :

  1. Utiliser un multi-stage build pour réduire la taille de l'image finale
  2. Choisir une image de base légère et sécurisée (Alpine ou Distroless selon le contexte)
  3. Optimiser le cache des layers Docker (dépendances avant code source)
  4. Créer un utilisateur non-root pour l'exécution
  5. Inclure un HEALTHCHECK adapté au type d'application
  6. Définir les variables d'environnement nécessaires avec des valeurs par défaut sensées
  7. Ajouter un fichier .dockerignore recommandé
  8. Gérer proprement les signaux d'arrêt (graceful shutdown avec tini ou dumb-init si nécessaire)

Pour chaque instruction du Dockerfile, ajoute un commentaire expliquant son rôle. Après le Dockerfile, fournis :

  • Le fichier .dockerignore correspondant
  • La commande docker build recommandée avec les arguments utiles
  • La commande docker run avec les flags de sécurité recommandés (--read-only, --cap-drop, etc.)
  • Un docker-compose.yml minimal si des services externes sont mentionnés
  • Les optimisations spécifiques au langage choisi (ex : pip --no-cache-dir pour Python, npm ci --omit=dev pour Node.js)

Personalize this prompt with Léa

Answer 3 questions and Léa tailors the prompt to your situation.

Why this prompt works

<p>This prompt guides the AI to produce a <strong>complete, production-ready Dockerfile</strong>. By specifying your language, framework and application type, you get a custom configuration that follows containerization best practices.</p><p>The variables let you adapt the output to any tech stack. The prompt emphasizes often-overlooked aspects: <strong>multi-stage builds</strong> to reduce image size, running as a <strong>non-root user</strong> for security, and optimizing <strong>layer caching</strong> to speed up successive builds.</p><p>Beyond the Dockerfile itself, you will also get the <strong>.dockerignore</strong>, secure build and run commands, and a docker-compose if needed. It is a solid starting point that you can refine according to your infrastructure constraints.</p>

Use Cases

Containerize a new application before its first deploymentOptimize an existing Dockerfile that produces oversized imagesSecure a Docker image for a production environmentConfigure a multi-stage build to separate build and runtime

Expected Output

A commented Dockerfile with multi-stage build, a .dockerignore file, recommended docker build and run commands, and optionally a docker-compose.yml. Each technical choice is explained with a comment.

Improve this prompt

Run this prompt through the Optimizer to strengthen its context, constraints and expected format.

Improve this prompt with the Optimizer

Comments

  • LéaAI

    Ajoutez `--platform=linux/amd64` dans `FROM` si vous déployez sur ARM (ex: M1) via émulation, pour éviter des incompatibilités silencieuses. Testez aussi `docker scout` après le build pour détecter les failles CVE.

Glossary Terms

ContextEmbeddingFew-Shot PromptingFine-TuningGPTGuardrailsInferenceLLM

📬 Get new prompts every week

Join our newsletter and never miss a prompt.

Go further

Improve this prompt with the Optimizer

Analyze the prompt, strengthen the context and get a more precise version.

Prompt Engineering

Cross-model principles for writing more reliable prompts.

Similar Prompts

💻DeveloppementBeginnerAll AIs

Create Database Seed Data

Generate realistic test data for development

0106
💻DeveloppementIntermediateAll AIs

Stable Diffusion Prompt for Documenting Code

Stable Diffusion, though primarily known for generating artistic images, can become a powerful tool for creating visuals to accompany code documentation. By generating conceptual diagrams, data flow illustrations, visual representations of software architectures, or thematic icons, Stable Diffusion helps enrich technical documentation that is often dry and hard to grasp. Developers and technical writers can thus produce explanatory schematics, process infographics, or illustrations of abstract concepts like design patterns, data structures, or execution flows. The major advantage lies in production speed: where a graphic designer would take hours to create custom visuals, Stable Diffusion generates illustrations in seconds, allowing rapid iteration until the desired result is achieved. This approach is particularly useful for teams documenting APIs, open-source libraries, or distributed systems, where visual clarity makes all the difference between ignored documentation and documentation developers actually consult.

0106
💻DeveloppementBeginnerAll AIs

Artifact Retention Policy

Optimize artifact storage costs

096
💻DeveloppementAdvancedAll AIs

Event Correlation for RCA

Root cause analysis for complex incidents

0100