To mitigate the issue of hallucinations in language models, it's effective to provide factual context in the prompts given to the model, which can lead to more accurate outputs.

In simulation, we can replay trajectories and vary motions without needing to demonstrate them repeatedly with a real robot, enhancing efficiency in training.

Using an agent is beneficial when you can't enumerate all possible situations, allowing the model to navigate unknown scenarios using its own intuition.

By training neural networks in diverse simulated environments, we can prepare them to handle real-world scenarios, effectively creating a digital twin of the robot and its environment, as demonstrated by humanoid robots that trained for 10 years in just two hours of simulation time to learn walking, showcasing the efficiency of simulation in robotics training.

The skill of prompting AI models is crucial; those who understand how to communicate effectively with these models can create more sophisticated and creative outputs.

Chain of thought prompting, where you instruct the model to think step-by-step, is less necessary with modern reasoning models but can still be useful in some cases. If you're running thousands or millions of inputs through your prompt, you'll still need to use classical prompting techniques to make your prompt more robust.

Using large language models as prototyping tools can be effective; they allow for rapid development and testing of ideas before creating more specialized models tailored to specific tasks.

It's beneficial for AI applications to infer context and suggest prompts dynamically, rather than relying solely on static examples.

By allowing models to think longer, large language models can leverage more compute on the fly, enhancing their intelligence for complex problems.