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

Prompt engineering is crucial; bad prompts can lead to 0% accuracy, while good prompts can boost performance to 90%.

Using a larger model for meta prompting can yield better prompts that are then distilled into smaller models for practical use.

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.

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.

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.

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

Meta prompting involves giving a role to the AI, such as an expert prompt engineer, to enhance the quality of prompts.

The skill of prompting is about introspection, understanding what you want, and making yourself legible to the model.