Intermediate reasoning steps generated by a model when solving complex, multi-step queries.
Chain-of-thought refers to intermediate reasoning steps generated while a model works toward an answer. Research has examined prompting models with such steps on multi-stage tasks. The concept helps explain one way a model may organize a calculation or problem, but it is not a guarantee of correctness.
A written explanation should not be treated as a complete, faithful record of what caused the model’s answer. Research has shown that plausible step-by-step explanations can omit influences or rationalize an incorrect conclusion. Evaluate the evidence and result rather than accepting a convincing narrative as proof.
The reasoning a product exposes can vary. A user-facing explanation or summary should not be assumed to reveal private internal processing. For practical work, request the assumptions, relevant evidence and a checkable calculation needed to assess the answer rather than depending on access to an internal chain.
Takeaway: Use concise, verifiable explanations to review AI-assisted work. Confirm important calculations and claims independently, especially when a fluent explanation could hide a mistaken assumption.
Example
An assistant estimates material quantities and gives a tidy sequence of arithmetic. A reviewer should check the input dimensions, units and allowance as well as the arithmetic. A logically presented explanation can still produce the wrong quantity if the starting measurements are wrong.
What to check
Ask for the key assumptions and an auditable result. Keep confidence separate from validation, and do not describe a displayed explanation as guaranteed access to the model’s actual internal decision process.
For more detail, see Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.