Multi Model Prompt Optimization With Iterative Refinement

via “instruction optimization via miprov2”

Stanford framework that replaces manual prompting with automatically optimized LLM programs.

Unique: Treats instructions as learnable parameters and uses gradient-free search (Bayesian optimization, genetic algorithms) to explore instruction space, discovering prompts that outperform human-written templates. Unlike static prompt libraries, MIPROv2 adapts instructions to specific tasks and metrics.

vs others: More sophisticated than few-shot example selection alone, MIPROv2 jointly optimizes instructions and examples, often achieving 5-20% performance improvements over hand-crafted prompts on complex tasks.