Including additional information or context in prompts significantly improves the model's understanding and output quality.

Hallucinations are a significant challenge for AI models, and we have been studying how to improve their truthfulness and express uncertainty.

When providing context, it's often best to place it at the beginning of the prompt to ensure the model retains focus on the task.

In medicine, hallucinations from AI can be life-threatening, so we ensure our models are trained exclusively on peer-reviewed medical knowledge to maintain accuracy.

While hallucinations are undesirable for factual accuracy, they can be beneficial in creative scenarios, where they can lead to innovative solutions.

By prompting the model with different language, we can simulate counterfactuals and complex scenes, allowing for the generation of varied scenarios based on the same initial conditions and enabling a robot to interact with objects as if it were in the real world, even if the hardware doesn't support it.

It's crucial to understand the complexities of working with AI systems, including the need to manage issues like hallucinations and model evaluations effectively.

The concept of hallucinations in AI refers to the models generating incorrect or fabricated information, which has already led to legal issues, such as lawyers submitting false court citations.

As models improve, it's often more effective to communicate the specific task directly rather than using metaphors or role-based prompts.