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

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

Including additional information or context in prompts significantly improves the model's understanding and output quality.

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.

Providing additional information and examples significantly improves the results in conversational prompt engineering.

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

The skill of prompting is about introspection, understanding what you want, and making yourself legible 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, I try to give examples that are not too similar to the data the model will see, to avoid overly consistent responses.