Chain of Thought
Chain of thought is a prompting technique that encourages large language models to break down complex reasoning into intermediate steps. This approach significantly improves performance on math, logic, and multi-step reasoning tasks.
Understanding Chain of Thought
Chain of thought prompting dramatically improves the reasoning capabilities of large language models by instructing them to articulate intermediate steps before arriving at a final answer. When asked a multi-step math problem, a model using chain of thought will show its work, breaking the problem into sequential sub-calculations that build on previous results. Research has shown this approach significantly boosts performance on arithmetic, commonsense reasoning, and logic tasks, sometimes transforming models from near-random to human-level accuracy. The technique works because it decomposes complex problems into manageable steps within the model's context window. Variants include zero-shot chain of thought (adding "let's think step by step"), self-consistency (sampling multiple reasoning paths and voting), and tree of thought (exploring branching strategies). Chain of thought has become a core component of modern agent architectures and is closely related to AI alignment efforts that value transparent reasoning.
Category
Generative AI
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