Structuring the reasoning in prompts helps the model perform better, even if it's not strictly reasoning as we define it.

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

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

For models like GPT-4, it's still worth asking it to think step by step, but for other models, you might not need to.

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.

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

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

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.