Chain-of-Thought
Explains Chain-of-Thought (CoT) prompting: why having a large language model write out its reasoning steps before answering raises accuracy, which tasks genuinely benefit, and the three misconceptions beginners run into most.
Chain-of-Thought asks a large language model to think before it answers: a math problem, a code fix, or a multi-step inference is split into explicit intermediate steps, and the model continues along its own derivation — often far more accurate. This topic covers the core mechanism, the zero-shot and few-shot styles, the tasks where it genuinely helps, and the boundary between seemingly thoughtful and truly reliable reasoning.
