Chain-of-Thought (CoT) Display is a technique used in AI, particularly with LLMs to enhance reasoning and problem solving. By explicitly structuring intermediate reasoning steps, CoT enables AI systems to handle complex tasks more effectively and provide more transparent, structured outputs for users.

𝗪𝗵𝘆 𝗨𝘀𝗲 𝗶𝘁?

• Builds Trust: Explaining recommendations helps users understand AI’s reasoning and how it arrived at its recommendations

• Encourages Feedback: Transparency empowers users to refine preferences.

• Boosts Engagement: A study on the Thought Spot mental health platform found that structured interfaces improved user engagement by 3,696 clicks and 293 searches, guiding users’ thought processes and decisions.

𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗶𝘁?

• Complex Problem Solving: For multi-step math problems or logical puzzles.

• AI-Assisted Tutoring: When AI teaches concepts step by step.

• Medium to High-Stakes Recommendation System – When AI suggests options and must justify them.

• AI-Assisted Creativity: When AI generates ideas, helping users refine results.

• Interactive Conversational AI – When AI reasons through dialogue instead of giving direct

How to use the pattern

• Progressive Disclosure – Start with a high-level summary, and allow users to expand details as needed.

• Conversational Language – Structure dialogues to mimic natural conversation and logical flow.

• AI Interaction Transparency – Clearly display external tools and data sources the AI uses to generate recommendations.

• Clear Presentation – Use tables or bullet points to organize reasoning steps for easy readability.

• Show Status : Showing statuses like “researching” and “reasoning,” the interface clearly communicates that it is actively processing the request, which helps reduce user uncertainty and makes wait times feel shorter.