The integration of large language models (LLMs) into mental healthcare and research represents a transformative shift, offering enhanced access to care, efficient data collection, and innovative therapeutic tools. This paper reviews the development, function, and growing use of LLMs in psychiatry, highlighting their potential to improve diagnostic accuracy, personalize care, and streamline administrative processes. However, LLMs also introduce challenges such as computational demands, potential misinterpretation, and ethical concerns, necessitating the development of pragmatic frameworks to ensure safe deployment. The paper explores both the benefits and risks of LLMs, including predictive analytics and therapy chatbots, while advocating for responsible AI practices to mitigate risks and maximize the potential of LLMs in advancing mental health. Key considerations include equitable access, managerial and process-related impacts, population mental health risks, and the need for robust monitoring systems. The paper concludes by emphasizing the importance of responsible guardrails, such as red-teaming and multi-stakeholder-oriented safety, to ensure the safe and effective use of LLMs in psychiatric care and research.The integration of large language models (LLMs) into mental healthcare and research represents a transformative shift, offering enhanced access to care, efficient data collection, and innovative therapeutic tools. This paper reviews the development, function, and growing use of LLMs in psychiatry, highlighting their potential to improve diagnostic accuracy, personalize care, and streamline administrative processes. However, LLMs also introduce challenges such as computational demands, potential misinterpretation, and ethical concerns, necessitating the development of pragmatic frameworks to ensure safe deployment. The paper explores both the benefits and risks of LLMs, including predictive analytics and therapy chatbots, while advocating for responsible AI practices to mitigate risks and maximize the potential of LLMs in advancing mental health. Key considerations include equitable access, managerial and process-related impacts, population mental health risks, and the need for robust monitoring systems. The paper concludes by emphasizing the importance of responsible guardrails, such as red-teaming and multi-stakeholder-oriented safety, to ensure the safe and effective use of LLMs in psychiatric care and research.