Chain-of-Thought

Chain-of-thought (CoT) is a prompting technique where the model reasons through intermediate steps before producing the final answer — improving accuracy on complex tasks.

Models trained to 'think out loud' before answering perform measurably better on math, logic, and multi-step reasoning. Modern reasoning models (Claude 4.7, GPT-5, o-series) bake CoT into the API — you can ask for 'thinking' separately from the final answer. CoT is the dominant pattern for hard reasoning. Cost is more output tokens; the win is fewer wrong answers.

Example

Asked '12% tip on $87.40 = ?', a CoT model first writes '0.12 × 87.40 = 8.40 + 1.05 = 10.49' before answering '$10.49' — and gets it right where one-shot answers often don't.

How OpenLabor uses it

OpenLabor uses CoT-mode on reasoning-heavy missions and skips it for cheap repetitive ones.

Does CoT help on every task?

No. On simple lookups it adds cost without quality. Reserve for tasks with measurable reasoning steps.

Related: llm, planner-executor.

AI Labor Glossary