Prompt-as-Code Practice

A Prompt-as-Code Practice is a software engineering practice that treats AI prompts as version-controlled code artifacts within software packages.

• AKA: Prompt as Code, Prompts as Code, Prompt-as-Software, Code-Managed Prompts, Prompt Code Management.
• Context:
  • It can typically store Static AI Prompts in version control repositorys alongside source code.
  • It can typically package AI Prompt Resource Bundles with Python packages for distribution.
  • It can typically enforce Prompt Governance Frameworks through code review processes.
  • It can typically support Prompt Organization Systems via file system hierarchys.
  • It can typically enable Prompt Versioning Processes through git workflows.
  • It can often facilitate Prompt Loading Mechanisms with import statements.
  • It can often provide Prompt Testing Frameworks through unit tests.
  • It can often implement Continuous Integration for prompt validation.
  • It can range from being a Simple Prompt-as-Code Practice to being a Complex Prompt-as-Code Practice, depending on its infrastructure complexity.
  • It can range from being a Manual Prompt-as-Code Practice to being an Automated Prompt-as-Code Practice, depending on its automation level.
  • It can range from being a Single-Repository Prompt-as-Code Practice to being a Multi-Repository Prompt-as-Code Practice, depending on its distribution scope.
  • It can range from being a Lightweight Prompt-as-Code Practice to being an Enterprise Prompt-as-Code Practice, depending on its organizational scale.
  • ...
• Examples:
  • Version-Controlled Prompt Implementations, such as:
    • LangChain prompts/ Directory in git repository demonstrating typical prompt storage.
    • OpenAI Function Library with semantic versioning demonstrating typical prompt packaging.
    • Anthropic Claude Prompt Collection with pull request workflow demonstrating typical governance enforcement.
  • Package-Distributed Prompt Systems, such as:
    • pip-installable Prompt Package demonstrating typical Python packaging.
    • npm Prompt Module with package.json demonstrating typical distribution.
    • Docker Container Prompt Bundle demonstrating containerized distribution.
  • Enterprise Prompt-as-Code Deployments, such as:
    • Google Internal Prompt Infrastructure demonstrating typical version control.
    • Microsoft Azure Prompt CI/CD demonstrating often-used continuous integration.
    • AWS Prompt Package Registry demonstrating often-used testing framework.
  • ...
• Counter-Examples:
  • Ad-hoc Prompt Management, which lacks version control and systematic organization.
  • Dynamic Prompt Generation, which creates prompts at runtime rather than packaging them.
  • Manual Prompt Configuration, which relies on user input rather than code artifacts.
• See: Software Engineering Practice, AI Development Practice, Text-to-* Model Prompt Programming Task, Static AI Prompt, Prompt Versioning Process, Prompt Organization System, Prompt Loading Mechanism, Prompt Governance Framework, Python Prompt Package System, AI Prompt Resource Bundle, Versioned AI Prompt Repository, Version Control System, DevOps Practice.

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