The clarification flow in Deep Research allows the model to ask users for more information, enhancing the interaction and improving the accuracy of the responses. To get the best response from the research model, the prompt needs to be well-specified and detailed, as users typically don't provide all necessary information in the first prompt.

If users could edit the system prompt, they could tailor AI responses to better match their communication style, enhancing the overall experience.

The shift in responsibility from developers to users regarding AI outputs allows for more powerful and flexible applications, as users can directly interact with and modify system prompts.

Feedback mechanisms in AI interfaces can help users refine their prompts and improve interaction with the AI, enhancing the overall experience.

When developing a new large language model product, it's essential to iterate on prompts to find what works best for your specific use case.

Reading prompts and model outputs closely helps improve prompting skills; experimentation is key.

Developer tools for prompt engineering are gaining traction, allowing users to chain prompts and test their effectiveness, which is appealing to college students experimenting with AI.

The future of prompting may involve more guided interactions with the model, rather than just typing into a text box.

We've developed a 'prompt to leaderboard' system that identifies which AI models are best suited for specific prompts, even if those prompts have never been seen before.