Chain-of-thought (CoT) prompts break complex problems into sequential steps so large language models can reason more reliably. First proposed by Wei et al. in 20221, CoT underpins OpenAI's reasoning models2 and the o1 series3. It improves accuracy on math, logic and science benchmarks4 by guiding the model through an ordered thought process.
Explicit vs implicit CoT
Explicit CoT tells the model to "think step by step." Implicit CoT uses few-shot examples or natural language inference to show rather than tell. Both approaches encourage systematic reasoning and help the model justify its answer.
Variants
- Zero-shot CoT generates reasoning without prior examples5.
- Automatic CoT builds question-answer demonstrations that the model then follows6.
- Multimodal CoT combines text and images for tasks like troubleshooting or product support7.
Benefits
- Structured reasoning leads to coherent outputs, as seen with large models like PaLM8.
- Decomposing problems reduces errors in ambiguous tasks.
- Flexibility across different task types improves adaptability.
Limitations
- Additional reasoning steps increase compute cost, slowing smaller models.
- Poorly designed prompts can produce irrelevant chains of thought.
Conclusion
CoT prompting is most valuable for complex decision making where transparency and reliability matter. Enterprises can combine CoT with retrieval or interactive querying to maximise performance.