Chain-of-Thought
Theory
When a model emits only the final answer, errors from early steps propagate silently. Chain-of-thought (CoT) makes intermediate reasoning explicit in the context window.
Without CoT
Prompt: "4 apples at $0.50 + 2 oranges at $0.75 — total?"
Output: $3.25 ❌
No visible scaffolding. Bad arithmetic step is invisible.
With CoT
Prompt: "...Let's think step by step."
Output:
- apples = 4 × $0.50 = $2.00
- oranges = 2 × $0.75 = $1.50
- total = $3.50 ✓
Each token conditions on correct prior state.
- Identify the sub-problems — apples cost, oranges cost, total.
- Solve each in order — write the arithmetic for each step.
- Aggregate — combine the intermediate results into the final answer.
CoT helps on arithmetic, symbolic, and multi-hop tasks. It adds latency on lookups. Reach for it on complex reasoning, not as a universal fix.