When There's No Standard Answer, Can AI Still Deliver?
When faced with math problems or code bugs, today's AI is already remarkably smart. But in real life, the questions we throw at AI are often open-ended: How can I improve myself during a 2-hour daily commute? What's the best affordable skincare routine under $50 for sensitive skin?
These questions don't have absolute, standard answers. What users need isn't a safe, overly comprehensive 'technically correct but useless' response, but actionable advice grounded in real-life experience and solid judgment. We built this dataset to see if models can step out of their 'safe zones' and solve practical problems like an experienced human.
To make subjective 'usefulness' measurable, we invited seasoned experts across various domains to craft 'golden responses' based on real-world scenarios. They broke down their practical experience into dozens of fine-grained grading rubrics—ranging from 'whether implicit needs were identified' to 'whether the advice is actionable'—defining the exact criteria for a great answer.
The dataset spans across multiple verticals such as women's health, career advancement, pet care, and skincare—diving deep into highly specific life situations, like treatment decisions for young breast cancer patients or odor control in multi-cat households. You can explore the full landscape of these real-world questions in the bubble chart below: