A team is building a creative brainstorming assistant using a foundation model on Amazon Bedrock. During testing, they find the model's word choices feel too repetitive and constrained, always picking the single most probable next token. They want to allow the model to consider a wider pool of likely words to produce more varied and diverse suggestions, without directly manipulating the temperature setting. Which inference parameter should they adjust to achieve this?