Meta-prompting
An approach in which an AI model generates or optimises prompts for itself or for other tasks. This makes it possible to create more effective, adaptive prompts based on an analysis of previous results.
| Advantages | Disadvantages |
|---|---|
| Automates the creation and optimisation of prompts | Can be computationally expensive |
| Can find non-obvious but effective wording | Results can be less predictable or interpretable |
| Adapts to different types of tasks and contexts | Can make quality control and verification harder |
An example of Meta-prompting:
You are a meta-prompting system. Your task is to create the optimal prompt for the task of summarising scientific papers.
Perform the following steps:
1. Generate three different versions of a prompt for summarising scientific papers.
2. Analyse each prompt, stating its strengths and weaknesses.
3. Based on that analysis, create a new, improved prompt that combines the best elements of the previous versions.
4. Explain why the final version of the prompt should be more effective for the task of summarising scientific papers.