Why Prompt Design Matters and Works: A Complexity Analysis of Prompt Search Space in LLMs

Understand

Despite the remarkable successes of large language models (LLMs), the underlying Transformer architecture has inherent limitations in handling complex reasoning tasks.

• Chain-of-thought (CoT) prompting has emerged as a practical workaround, but most CoT-based methods rely on a single, generic prompt such as "think step by step", with no task-specific adaptation.
• These approaches expect the model to discover an effective reasoning path on its own, forcing it to search through a vast prompt space.
• In contrast, several studies have explored task-specific prompt designs to boost performance.