Why Prompt Optimization Works, and Why It Sometimes Doesn't: A Causal-Inspired Edit-Level Analysis

2026-05-26

AI Summary

This study addresses the limited generalizability of existing automatic prompt optimization methods across tasks and models. Adopting a causal inference perspective, it systematically investigates the interplay between prompt-editing behaviors and task characteristics across diverse optimization frameworks, large language models, and NLP benchmarks. By integrating complementary approaches—including propensity score adjustment, cognitive load annotations, surface textual features, and editing motifs—the work uncovers the structural causes underlying prompt optimization failures: edits that increase complexity or introduce meta-instructions impair performance on mathematical and multi-hop reasoning tasks, whereas step-by-step guidance and metacognitive edits substantially enhance logical and sequential reasoning. These findings demonstrate consistent and generalizable patterns across multiple experimental frameworks.

Abstract

Automated prompt optimization methods (e.g., DSpy, TextGrad) can substantially improve the performance of large language model (LLM), however, their generalization ability across different tasks remains underperformed. In practice, the superiority of the optimized prompt on one benchmark often fails to transfer to another, and this limitation persists even when switching across different LLM backbones. To investigate the underexplored sources of heterogeneity in prompt performance, we conduct a causal inference-inspired observational analysis of optimized prompts across a diverse set of optimization frameworks, LLM backbones, and NLP benchmarks. To achieve the goal, we build upon the propensity-adjusted associational analysis together with multiple complementary representations of prompt edits, where the consistent task-conditioned edits patterns are identified. We find that complexity-increasing and meta-instructional edits are negatively associated with mathematical and multi-hop reasoning performance, whereas step-by-step and meta-cognitive edits improve logical and sequential reasoning tasks. These effects are robust across cognitive-load annotations, surface-level text features, and edit-motif analyses, and can generalize across optimization frameworks. Overall, these results indicate that prompt optimization failures arise from systematic interactions between edit families and task characteristics rather than random optimization artifacts, providing feature-level characterization of optimizer behavior and motivating future task-conditioned optimizer design.

Problem

• prompt optimization
• generalization
• large language models
• task heterogeneity
• causal analysis

Innovation

• prompt optimization
• causal inference
• edit-level analysis
• task-conditioned generalization
• large language models