Chain of Thought Prompting

Chain of Thought Prompting is a prompt-engineering technique that instructs a large language model to reveal its step-by-step reasoning before giving the final answer. By adding cues such as “Let’s think step by step” or providing worked examples, the prompt triggers the model to generate intermediate thoughts—logical deductions, sub-calculations, citations—which improves accuracy on multi-hop questions, math problems, and logic puzzles. The explicit reasoning acts like scratch paper: it guides token probabilities toward correct conclusions and lets reviewers audit for errors or bias. Variants include few-shot CoT, where several solved examples teach the pattern, and self-consistency, which samples multiple chains and picks the most common result. Drawbacks are longer outputs and potential leakage of sensitive logic, mitigated by truncating or hiding the chain in production. Metrics such as exact-match, reasoning F1, and latency monitor impact. Chain of Thought Prompting turns opaque LLM guesses into transparent, verifiable solutions.