ModelRefs / Meta-Prompting — AI Glossary
Meta-Prompting — AI Glossary
Using an LLM to generate or improve prompts for another LLM (or itself), automating prompt optimization.
Overview
Meta-prompting frameworks (DSPy, AutoPrompt, Promptbreeder) treat prompts as learnable parameters and use LLMs to rewrite them based on eval feedback. Particularly effective for standardizing enterprise prompt libraries.
Reference details
| Topic | prompting |
|---|---|
| Last reviewed | 2026-06-24 |
Related terms
Commonly confused with
Using a model to write or improve prompts, as opposed to a person doing it. It differs from automated prompt optimisation frameworks in rigour rather than concept: those search against a scored dataset and keep what measurably wins, while asking a model to improve a prompt returns something that reads better with no evidence it performs better. Without an evaluation set it is a rewrite, not an optimisation.
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Frequently asked questions
What is Meta-Prompting?
Using an LLM to generate or improve prompts for another LLM (or itself), automating prompt optimization.
What concepts are related to Meta-Prompting?
Closely related concepts include prompt engineering, prompt management.