Optimize prompts with meta-prompting against an eval set instead of hand-tuning few-shot examples

K2view·low signal

Meta-prompting — having a model generate and iteratively refine prompts scored against an eval set, rather than hand-tuning — is increasingly the 2026 default for prompt optimization, cutting manual effort and the bias that hand-picked few-shot examples introduce. The actionable loop: define a small eval set, let a model propose prompt variants, score them, and keep the winner. It also saves tokens versus stuffing many few-shot demonstrations into every production call.