1. Why “reasoning prompts” matter

Most LLM failures are not about knowledge, they are about thinking:

• They jump to the first plausible answer.
• They fail on multi-step logic.

2. Chain-of-Thought (CoT): step-by-step reasoning

2.1. Intuition

LLMs are next-token predictors trained on tons of worked examples:

• Math solutions
• Code explanations
• Logical proofs
• Step-by-step derivations

When you say “think step by step”, you are aligning your query with that training distribution:

• Without CoT: the model tries to jump directly to the final answer.
• With CoT: the model is incentivized to reconstruct a plausible reasoning trace, similar to textbook solutions.

This has 2 key effects:

1. Regularization of reasoning: Forcing intermediate steps reduces the chance of a single bad “global guess.”