Google's latest Gemini models have deprecated and are ignoring the temperature, top-p, and top-k sampling parameters, a move speculated to improve determinism and align with reinforcement learning training. Developers are frustrated by the reduction in output diversity for complex tasks, leading them to adopt prompt engineering workarounds like injecting random variations into their queries. This shift mirrors similar recent changes by other major AI providers and has sparked community debates over the restriction of generation parameters.