Prompt Engineering for Production Systems
Production prompt engineering is not the same skill as demo prompt engineering. In production, prompts need to be reliable across a distribution of inputs, maintainable by a team, version-controlled, and testable. This guide covers systematic prompt design methodology, the specific patterns we use in production, and the tooling and workflow that keeps prompts reliable at scale.
What's inside
Part 1: Production vs demo prompt engineering - why the same prompt fails in production
Part 2: Systematic prompt design methodology (pages 5-9) - structure, persona, constraints, output format
Part 3: Production prompt patterns (pages 10-14) - chain-of-thought, self-critique, citation enforcement, structured output
Part 4: Testing and evaluation (pages 15-17) - building a prompt test suite, evaluating regressions
Part 5: Versioning and team workflow (pages 18-20) - prompt management, review processes, rollback procedures
What you'll get
Prompt design template: a structured framework for writing production-grade prompts
Pattern library: 12 prompt patterns with worked examples and when to use each
Prompt test suite design guide: how to build a test set that catches regression before deployment
Versioning workflow: Git-based prompt management with review and approval process
Evaluation rubric: how to assess prompt quality beyond 'does it work in this example?'
Anti-pattern reference: 15 common prompt engineering mistakes and how to avoid them
Who this is for
Engineers writing prompts for production LLM applications
AI teams building systems that need reliable, maintainable prompt logic
Technical leads defining prompt engineering standards for their team
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Prompt Engineering for Production Systems
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