Best practices for prompt versioning and testing?

Prompt management is often overlooked but critical for production AI. Here's our workflow:

1. Version Control: Store prompts as code, not in databases or config files. Git history tracks all changes. Code review for prompt changes. Easy rollback if needed.

2. A/B Testing: Use feature flags to test prompt variations. Measure: Response quality (human evaluation), Task completion rate, User satisfaction scores, Token usage (cost).

3. Regression Testing: Create a test suite with expected outputs. Run tests before deploying prompt changes.

4. Team Collaboration:

• Prompt library: Centralized repository of all prompts
• Documentation: Include purpose, examples, and known limitations
• Review process: Require approval for prompt changes
• Prompt playground: Internal tool for testing prompts before deployment

Tools:

• LangSmith: Prompt management and testing platform
• PromptLayer: Logging and version control for prompts
• Weights & Biases: Track prompt performance metrics
• Custom solution: Build internal prompt management system

Our Stack:

1. Git for version control
2. Feature flags for A/B testing (LaunchDarkly)
3. Custom test suite in pytest
4. Notion for prompt documentation
5. Internal Streamlit app for prompt playground

Pro tip: Treat prompts like code. They deserve the same rigor as your application logic.