Meta-prompting uses an LLM to generate, evaluate, and improve prompts. Instead of manually writing prompts, you describe the task and let the model generate a better prompt. DSPy (Declarative Self-improving Python) automates prompt optimisation — you write the program structure, DSPy optimises the prompts using your evaluation set. Automatic Prompt Optimisation (APO) iterates: generate prompt → evaluate on test set → identify failures → improve prompt → repeat.
How DSPy separates program structure from prompts.
When your prompt performs poorly, ask GPT-4o to critique it given the failure examples. 'Given these misclassifications, what's unclear or missing in my prompt?' often produces insights faster than manual analysis.
With DSPy, you write the program structure (retrieve, generate, score) and DSPy generates the optimal prompts automatically against your eval set. Code changes without prompt rewrites.
Meta-prompting's power comes from automated evaluation. Without a labeled dataset to measure prompt quality objectively, you're optimising in the dark. Build the eval set first, optimize second.
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