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

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.

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

The five effective prompting techniques are: 1) Few-shot prompting, where you give examples of what good looks like; 2) Decomposition, which involves solving sub-problems first; 3) Self-criticism, where you check and reflect on your response; 4) Providing additional context about the problem; and 5) Ensembling, where you try different roles and models to find common answers.

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.

A good prompt engineer should be able to step back from their own knowledge and communicate the necessary information clearly to the model.

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.

When prompting, it's important to give the model an 'out' for unexpected inputs, allowing it to indicate uncertainty when necessary.

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