Prompt minimization shows which parts of few-shot LLM inputs matter
How Much Prompt Is Enough? A Blackbox Minimization of Few-Shots in LLMs
Software EngineeringArtificial IntelligenceHuman-Computer Interaction
Summary
Prompts guide how large language models (LLMs) respond, but it's unclear which parts of prompts are really needed. The authors present a method called ramework that figures out the smallest pieces of a few-shot prompt you need to keep to get the same output. They found a big chunk of prompt text can be removed without changing results, usually keeping technical parts and dropping extra explanations. This helps engineers understand and shrink prompts for better reliability.
What this means in practice
- •For software engineers: Reduce complexity and size of few-shot prompts to improve reliability in language model-powered software.
- •For ai tool developers: Create prompt optimization tools that automatically minimize few-shot exemplars without loss of output quality.$Commercial implications: Enables commercial software that refines prompts for better efficiency and consistency in AI applications.
Authors
Ali Alfageeh, Rahul Gopinath, Amin Alipour
Abstract
Prompts are the primary mechanism for directing the behavior of large language models (LLMs). Yet the internal structure and causal hierarchy of prompts remain poorly understood: which parts are causally necessary and which are redundant is an open question. This opacity can have severe consequences. Subtle prompt variations can silently shift model outputs in critical software systems, and engineers lack techniques to reason about prompt reliability. We present \framework, a blackbox prompt-minimization framework that reduces few-shot prompts to their necessary minimal subset. We use a case study to apply \framework to a few-shot learning system and demonstrate the insights that this framework can provide. Our experiments show that few-shot exemplars can be reduced by a mean of 65.3\%~$\pm$~15.8\% in character count while fully preserving propositional output fidelity. The models preferentially retain logical identifiers and constraint declarations while discarding natural language prose and cross-prompt relational annotations. Our analysis also shows that some models are universal encoders, able to produce highly legible yet minimized prompts, while others are universal decoders, able to interpret minimized prompts from most other models. By identifying which components are indispensable, \framework provides a principled basis for prompt compression and structural analysis of few-shot exemplars.