What Is Chain-of-Thought (CoT)?
Chain-of-thought (CoT) is a prompting technique and model capability where the AI generates intermediate reasoning steps before arriving at a final answer, significantly improving performance on tasks requiring multi-step logic, mathematics, and complex reasoning.
How Chain-of-Thought (CoT) Works
When asked a complex question, humans do not jump straight to the answer — they reason through intermediate steps. Chain-of-thought brings this same approach to AI. By adding 'Let's think step by step' to a prompt, or by training models to naturally produce reasoning traces, the model breaks complex problems into manageable sub-problems and solves them sequentially. CoT was a major breakthrough: it dramatically improved LLM performance on math, logic, and reasoning tasks without any model changes — just by changing how the model was prompted. Modern models like OpenAI's o1 and o3 are specifically trained for deep chain-of-thought reasoning, spending more compute on 'thinking' to solve harder problems. CoT has become a fundamental technique in prompt engineering and model training.
Real-World Examples
Adding 'Let's think step by step' to a math problem prompt and seeing GPT-4's accuracy jump from 60% to 95%
OpenAI's o1 model generating a long internal chain-of-thought to solve a complex coding problem before outputting the solution
A chain-of-thought prompt for a legal question: 'First identify the relevant law, then apply it to the facts, then reach a conclusion'
Chain-of-Thought (CoT) on Vincony
Vincony's Compare Chat lets users test chain-of-thought prompts across different models to see which ones reason most effectively step by step.
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