When prompting, I try to give examples that are not too similar to the data the model will see, to avoid overly consistent responses.

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

In the future, prompting may involve the model eliciting information from us rather than us having to specify everything.

There's a risk of overcomplicating prompts by trying to anticipate every possible issue the model might face, which can lead to ineffective results.

A good prompt engineer must communicate clearly, iterate on prompts, and anticipate potential misinterpretations by the model.

You can enhance your prompting by including examples in your prompt. For instance, provide a problem and a good answer, and then ask the model to solve a similar problem.

It's crucial to think about edge cases when designing prompts, ensuring they work not just for typical scenarios but also for unusual inputs.

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

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.