Meta-prompting
Also called prompt optimisation, prompt rewriting
Using an AI model to write, improve or critique prompts, rather than doing the task directly.
Meta-prompting means prompting about prompts. Instead of asking a model to write the email, you ask it to write a better prompt for writing the email, or to critique your existing prompt and suggest improvements. The output is a prompt you then use, often in a fresh conversation.
It works because models are good at spotting what is missing from instructions: unclear audience, no output format, vague success criteria. A model can also suggest structure, such as sections, tags or examples, that you might not think to add. Several AI providers include prompt-improvement tools in their developer consoles for this reason.
The main limit is that a model can not know facts about your situation that you have not given it. A meta-prompt that asks the model to first ask you questions tends to produce better prompts than one that just rewrites. And a rewritten prompt still needs testing; longer and more formal does not mean better.
Example
"Here is my prompt for summarising sales calls. Ask me up to three questions about anything missing, then rewrite it with a clear output format and one example." The model asks who reads the summaries and what decisions they make, then produces a tighter prompt.
Common mistake
Accepting the improved prompt without testing it. Model-written prompts often look impressive but add requirements you never wanted, or bloat a simple task. Run the old and new prompts on the same few inputs and compare the results before switching.