chain-of-thought

Step-by-step reasoning reveals a model's thinking process. Inject structured chain-of-thought blocks using XML tags like <thinking>...</thinking> to separate reasoning from final output, making complex problem-solving transparent and verifiable.

Chain-of-thought prompting helps when analyzing code errors or tracing logic. By requiring the model to work through observations and reflection before answering, you expose reasoning gaps and improve diagnosis accuracy in debugging tasks.

Use nested XML thinking blocks with advanced tags—plan, reflection, observation—to organize multi-step reasoning. Markdown formatting within blocks keeps traces readable while maintaining clear separation between working notes and final answers.

Yes. Chain-of-thought works across research synthesis, decision-making, and complex problem-solving. Structured thinking blocks force the model to justify conclusions by showing evidence collection and reasoning stages before delivering output.

Chain-of-thought enhances code analysis by requiring the model to trace logic paths and explain observations before conclusions. This structured reasoning catches reasoning errors and produces more reliable analysis than direct answers.

Regular prompting requests direct answers; chain-of-thought adds explicit thinking blocks that separate reasoning from output. This structured transparency improves accuracy on complex tasks by forcing models to justify each step rather than skip to conclusions.