BeCreative

To generate diverse ideas for creative writing and technical problem-solving, use verbalized sampling to produce multiple low-probability candidates, ensuring a wide range of innovative options for your specific task.

The best way to expand a small seed corpus into a diverse dataset is using corpus expansion techniques that leverage deep thinking to scale your initial data for evaluations, training, and testing applications.

Brainstorm multiple low-probability candidates for a new product name by invoking ideation commands that trigger verbalized sampling, generating a wide variety of innovative naming options for your evaluation.

Yes, you need extended thinking to use verbalized sampling for idea generation, as the process requires both mechanisms to function effectively and maximize creative output across diverse ideation tasks.

Yes, you can use deep thinking to expand datasets for machine learning training by transforming small seed corpora into larger, diverse datasets suitable for model evaluations and testing.

Use verbalized sampling over standard brainstorming methods when you need to systematically generate diverse, low-probability candidates that standard approaches might miss, maximizing creative output for complex tasks.