In AI systems, Chain-of-thought (CoT) prompting technique enhances the model’s ability to solve complex problems by mimicking a more structured, step-by-step thought process like that of a human.
CoT display is a UX pattern that improves transparency by revealing how the AI arrived at its conclusions. This fosters user trust, supports interpretability, and opens up space for user feedback especially in high-stakes or ambiguous scenarios.
E.g., Perplexity enhances transparency by displaying its processing steps helping users understand the thoughtful process behind the answers.
E.g., Khanmigo an AI Tutoring system guides students step-by-step through problems, mimicking human reasoning to enhance understanding and learning.
How to use this pattern
Show status like “researching” and “reasoning to communicate progress, reduce user uncertainty and wait times feel shorter.
Use progressive disclosure: Start with a high-level summary, and allow users to expand details as needed.
Provide AI tooling transparency: Clearly display external tools and data sources the AI uses to generate recommendations.
Show confidence & uncertainty: Indicate AI confidence levels and highlight uncertainties when relevant.