If you're building AI features, you may experience unexpected changes in output when tweaking a prompt that was working perfectly in development and pushing it to production. This is due to the non-deterministic nature of large language models (LLMs), where tiny adjustments can have major ripple effects. To address this, prompt versioning and management are essential components of a larger system called prompt management. Prompt versioning tracks changes to your prompts over time, including version history, ability to roll back to previous versions, testing prompts before deploying changes, managing different prompt variations for A/B testing, tracking which prompt versions are running in different environments, and more. Proper prompt versioning addresses pain points such as transparency, accountability, reliability and trust, consistency in AI outputs, reproducibility of results, improved experimentation, collaboration, targeted AI experiences, and performance tracking. Common challenges encountered without proper versioning include organizational confusion, reproducibility issues, time inefficiencies, dependency management, and performance tracking. To implement prompt versioning, strategies such as smart labeling conventions, structured documentation, AI configurations, collaborative workflows, testing and validation, monitoring, version control integration, and environment management can be employed. These approaches help ensure reliability, consistency, and transparency in AI development, ultimately leading to better user experiences, reduced headaches, and increased team productivity.