How do reasoning-capable LLMs perform chain-of-thought reasoning?

Chain-of-thought reasoning encourages LLMs to generate intermediate reasoning steps before producing final answers.

Standard prompting often causes models to jump directly to answers, which may reduce reasoning quality for complex tasks.

Chain-of-thought (CoT) prompting improves reasoning by encouraging explicit intermediate steps. Instead of only predicting final outputs, the model decomposes problems into smaller logical stages.

The process works because:

Variants include:

4.…