Chain-of-Thought (CoT)

Chain-of-thought (CoT) is a prompting technique that significantly improves the reasoning capabilities of large language models by encouraging them to generate explicit intermediate reasoning steps before arriving at a final answer. Rather than jumping directly to conclusions, CoT prompting leads models through a structured problem-solving process that mirrors human analytical thinking.

The technique was formalized in research by Wei et al. at Google, who demonstrated that simply adding phrases like "Let's think step by step" to prompts could dramatically improve performance on mathematical, logical, and multi-step reasoning tasks. Few-shot CoT provides example reasoning chains alongside input-output pairs, while zero-shot CoT relies on the instruction alone to elicit step-by-step reasoning.

Advanced CoT variants have been developed to address different reasoning challenges. Tree-of-thought explores multiple reasoning paths in parallel, evaluating and pruning branches to find optimal solutions. Self-consistency generates multiple independent chains of thought and selects the most common conclusion, improving reliability through ensemble-like diversity. Program-of-thought translates reasoning steps into executable code, combining the flexibility of natural language reasoning with the precision of programmatic computation.

CoT is particularly valuable in enterprise applications involving complex decision-making, multi-step analysis, mathematical calculations, and logical reasoning. It also provides interpretability benefits, as the explicit reasoning chain allows humans to verify the model logic, identify errors, and build trust in AI-generated conclusions. Production implementations often combine CoT with output parsing to extract both the reasoning trace and the final answer, enabling logging and auditing of the model decision-making process.