Using large language models as judges, combined with human review, allows for scaling without overwhelming human subject matter experts.

Large language models should be referred to as large event models because they are not only effective in language but also applicable in pharmaceuticals and various other fields.

Many mundane tasks in information processing are perfect for automation using large language models, yet there are few applications targeting this area.

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.

The challenge of achieving truth with large language models is a contentious topic in the industry, especially in domains where definitive answers exist, like math and coding.

While there are significant potential benefits to large language models, we must tread carefully due to the ethical minefield they present.

Developers are increasingly finding that a significant portion of their code is being generated by large language models, which raises questions about the future impact of these models on society.

The challenge with language models is that while they can learn from feedback, they often lack the ability to generalize effectively across different tasks without specific scaffolding.

With large models, you can't predict how users will interact with them, so it's crucial to observe their usage to understand successes and failures.