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CI/CD Pipeline Prompt Template

Design a CI/CD pipeline with build, test, and deployment stages including rollback strategy and environment configuration.

The Prompt

ROLE: You are a DevOps engineer and platform reliability specialist who has built CI/CD pipelines that ship code to production dozens of times a day without causing incidents. CONTEXT: You are designing a CI/CD pipeline for a project that needs to move from code commit to production deployment safely and repeatably. The pipeline is the last line of defence before code reaches users — it must be fast enough that developers use it (not bypass it), and robust enough that it catches the issues that matter. TASK: Design a complete, production-ready CI/CD pipeline for the project and platform described below. RULES: • Every stage must have a clear pass/fail condition — not "run tests" but "tests must pass with >80% coverage and zero failed test cases" • The security scan stage is non-optional — include dependency vulnerability scanning at minimum • Environment variable management must distinguish between secrets (never in code or logs) and config (can be version-controlled) • The rollback strategy must be automatic for critical failures and manual for edge cases — specify which is which • Include pipeline run time estimate per stage and flag any stage that could be parallelised to reduce total time CONSTRAINTS: Generate a working YAML config for [CI_PLATFORM]. Use environment variable placeholders like ${{ secrets.KEY_NAME }} for sensitive va

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How to use this template

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Fill in the placeholders

Replace anything in [BRACKETS] with your specific details.

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Why this prompt works

Specifying pass/fail conditions per stage (not just stage names) forces the AI to design a pipeline that actually gates deployment rather than one that adds delays without adding safety. The secrets vs config distinction prevents the common failure of sensitive values ending up in logs.

Tips for best results

  • Add 'each stage should complete in under 5 minutes' to CONSTRAINTS for fast-feedback pipelines — slow CI is the #1 reason developers push directly to main
  • The first run of any generated pipeline usually needs minor fixes — add a 'pipeline health check' job that validates the config before adding more stages
  • Specify branch protection rules alongside the pipeline — a pipeline is useless if developers can bypass it by pushing directly to main
  • Ask the AI to add a 'deploy preview' stage for pull requests if your platform supports it — it dramatically reduces the time to catch UI and integration issues

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