Reasoning out loud
Chain of thought prompting asks the model to work through intermediate steps before giving a final answer. Instead of jumping to a conclusion, it writes the reasoning, which often improves accuracy on multi step problems like math word problems and logic puzzles.
Why it helps
- Decomposition breaks a hard problem into smaller manageable steps.
- Self correction can occur as the model notices a mistake mid reasoning.
- Visibility lets you inspect where the logic went wrong.
Triggering it
- A simple cue like let us think step by step often elicits reasoning.
- Few shot examples that show worked solutions teach the pattern.
- Asking for the final answer on its own line keeps output parseable.
Cautions
The written reasoning is not a guaranteed faithful account of the internal computation, so it can sound convincing yet be wrong. It also costs extra tokens and latency. For simple tasks, forcing reasoning adds overhead without benefit, so reserve it for problems that genuinely have multiple steps.
Key idea
Chain of thought asks the model to show intermediate steps, which lifts accuracy on multi step problems but adds cost and is not always a faithful trace of the real computation.