Definition: Chain-of-thought prompting asks a language model to work through intermediate reasoning steps before giving a final answer, which tends to improve accuracy on multi-step problems.
The technique was popularized by research showing that asking a model to reason step by step, or showing it worked examples, improved results on math and logic tasks. Many current models are trained to reason internally, often called reasoning models, and expose settings for how much reasoning effort to spend.
Reasoning steps add output tokens, which raises latency and cost. The written reasoning is not a guaranteed explanation of how the model reached its answer, so it should not be treated as an audit record on its own. Production systems often hide the reasoning from end users and show only the final, validated answer.
It helps most with calculations, planning, multi-hop questions and AI agents that must decide which tool to call next. For simple lookups or classification it adds cost without much benefit, which teams confirm with evals.
Ryz LLM engineers design prompts and reasoning settings for production agents and measure the trade-offs, working on your team.
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