Model temperature is a crucial parameter in language models, affecting the randomness and creativity of outputs in tasks such as text generation, summarization, and translation. Higher temperatures promote creative variance but increase error risk, whereas lower temperatures yield more predictable results. Finding the optimal temperature for a specific application can be challenging, akin to finding the perfect balance in a task's complexity and desired creativity. Statsig's Autotune, a Bayesian Multi-Armed Bandit tool, helps optimize this parameter by testing and adjusting variations to maximize a target metric, gradually allocating more traffic to better-performing treatments until a winning variation is determined. While Autotune is particularly effective for optimizing model temperature, it can also be applied to other parameters with a single quantifiable outcome. However, the tool's results may not provide a long-term solution across an entire application, necessitating continuous experimentation with multiple parameters. As AI models proliferate in production, continuous online testing becomes essential for measuring the impact of changes, and tools like Autotune offer AI developers a competitive edge in launching features.