Effective prompt management is crucial for optimizing the performance of large language models (LLMs) in production, as it involves a systematic approach to tracking, testing, and refining prompts. With the increasing complexity of prompts, both developers and non-technical stakeholders play significant roles in prompt design, necessitating tools that facilitate collaboration, version control, and experimentation. Helicone, among other tools, offers features like live previews, sandbox environments, and real-time updates to streamline prompt management, enabling developers to iterate independently of the code and collaborate efficiently with non-technical teams. Key aspects of prompt management include prompt engineering, which focuses on crafting prompts for optimal model output, and prompt testing and evaluation, which involves systematic testing to ensure accuracy and relevance. Additionally, preventing prompt injection attacks and avoiding common pitfalls, such as hardcoding prompts or not testing across models, are essential for maintaining AI application security and performance. As the landscape of LLMs continues to evolve, investing in a robust prompt management tool that offers flexibility, security, and ownership becomes increasingly important for building reliable AI systems.