Chain-of-Thought Prompting

A technique for improving the reasoning capabilities of large language models by instructing them to break down complex tasks into intermediate reasoning steps before producing a final answer. In accessibility applications, chain-of-thought prompting is used to improve the quality of AI-generated content such as image descriptions and variation analyses. For example, when comparing multiple image descriptions, a model can be prompted to first decompose each description into atomic facts, then cluster related facts, then identify agreements and disagreements, and finally produce a coherent summary. This step-by-step approach typically produces more accurate and structured outputs than direct prompting.