Large language model prompts trimmed without losing quality
Prompt Minimization: Reducing Input Redundancy Without Sacrificing Output Fidelity
Artificial Intelligence
Summary
Writing instructions for language AI often includes extra, unnecessary words. The authors studied how to shrink these instructions to the smallest version that still gets the same good answers from the AI. They found that many long instructions have extra parts that don’t help, and shorter ones can work just as well. This means faster and cheaper use of language AI with less confusion.
What this means in practice
- •For software developers: Create AI-powered apps that respond faster by using shorter prompts without losing answer quality.
- •For product managers: Design AI tools that reduce costs by minimizing prompt length while maintaining reliable output.
Authors
Marius F. R. Juston, Kevin A. Karim, Jonathan Gao, Kevin C. Li, Rudhi Bashambu
Abstract
Despite the growing capabilities of large language models (LLMs), prompt design remains largely heuristic and ad hoc. This project will explore $\textit{prompt minimization}$, the process of reducing prompts to their smallest, most information-dense form while preserving output fidelity. Practically, shorter prompts reduce computational overhead and inference latency, especially when large contexts, such as entire documents or codebases, are included unnecessarily. Further, longer prompts can damage LLM reasoning and accuracy. Theoretically, the existence of multiple prompts yielding equivalent outputs suggests a high degree of redundancy in the input space, raising fundamental questions about what information is essential to elicit specific model behaviors. We propose three variant frameworks to identify and evaluate minimal prompts and demonstrate that minimal prompts often produce outputs comparable to those of their longer counterparts. These findings suggest new directions for efficient prompt engineering and deepen our understanding of input compression in LLMs.