Characterizing Fitness Landscape Structures in Prompt Engineering (arxiv.org)

Researchers systematically probed the “fitness landscapes” of prompt engineering to understand how prompt semantics map to model performance. Using autocorrelation analysis in semantic embedding space, they measured how performance similarity between prompts changes with semantic distance. They compared two prompt-generation regimes—systematic enumeration (1,024 prompts) and novelty-driven diversification (1,000 prompts)—on 10 error-detection tasks. Systematic generation produced smoothly decaying autocorrelation (nearby prompts have similar performance), while diversified generation showed non-monotonic autocorrelation with a peak at intermediate distances, indicating rugged, hierarchical landscape structure. Different error types exhibited varying degrees of ruggedness. The work matters because it moves prompt optimization from black-box search toward landscape-aware strategy design. Smooth landscapes suggest local-search or gradient-like methods will be effective and sample-efficient; rugged, multi-scale landscapes imply the need for population-based, novelty-seeking, or hierarchical search to avoid deceptive local optima. Methodologically, applying autocorrelation over semantic embeddings provides a practical diagnostic to choose or adapt optimization algorithms for a given prompt task. Overall, the paper offers empirical foundations and tools that can make prompt tuning more predictable and guide algorithm selection for more efficient, robust prompt engineering.