Prompt management has become a crucial aspect of AI systems, influencing the performance and reliability of language models through structured workflows similar to software engineering practices. As AI prompts dictate model behavior by setting parameters and instructions, tools for managing these prompts have emerged to help teams organize, share, and refine them, ensuring consistent outputs and reproducibility. Central libraries, sandboxes, and feedback loops are key components of prompt management systems, providing environments for storing, testing, and improving prompts. Leading tools like Arize AX, Arize Phoenix, PromptLayer, DSPy, and PromptHub offer various features such as version control, analytics, and modular workflows, catering to diverse needs from enterprise-level deployments to open-source flexibility. The choice of tool depends on a team's specific requirements, whether they prioritize deep observability, hosted solutions, or collaborative environments, each facilitating the effective integration of prompts into AI workflows.