Chain of Thought (CoT) prompting encourages LLMs to show their reasoning step-by-step before giving a final answer. This improves accuracy on complex reasoning tasks.
How to Use CoT
- Zero-shot: Add "Let's think step by step"
- Few-shot: Provide examples with reasoning
- Self-consistency: Generate multiple chains, vote on answer
When CoT Helps
- Math and logic problems
- Multi-step reasoning
- Complex analysis
- Decision making
Does CoT prevent hallucinations?
CoT can help catch errors in reasoning but doesn't prevent hallucinations. The model may reason confidently from false premises.
Practical example
A task is broken into intermediate checks before producing a final calculation or answer.
What to check
Evaluate the final answer and externally verifiable intermediate results when they are available.
Limits and tradeoffs
A displayed reasoning narrative may be incomplete or incorrect; it is not a security audit trail.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.