What is Chain-of-Thought
Chain-of-Thought - Prompting models to show step-by-step reasoning
A prompting technique that instructs language models to break down complex problems into intermediate reasoning steps before producing a final answer. Chain-of-thought improves accuracy on reasoning tasks and makes model behavior more interpretable in multimodal AI systems.
How It Works
Chain-of-thought prompting includes examples or instructions that demonstrate step-by-step reasoning. Instead of directly outputting an answer, the model generates intermediate reasoning steps that lead to the conclusion. This explicit reasoning process helps the model handle multi-step problems, reduces errors from skipped logic, and makes the reasoning process auditable.
Technical Details
Approaches include few-shot CoT (providing reasoning examples in the prompt), zero-shot CoT (adding 'Let's think step by step'), and tree-of-thought (exploring multiple reasoning paths). Self-consistency samples multiple reasoning chains and selects the most common answer. CoT significantly improves performance on math, logic, and multi-hop reasoning tasks. The technique works best with larger models (>10B parameters) that have sufficient capacity for explicit reasoning.
Best Practices
- Use chain-of-thought for complex queries that require multi-step reasoning or analysis
- Provide clear reasoning examples in few-shot prompts for consistent step formatting
- Apply self-consistency by sampling multiple reasoning chains and taking majority vote
- Log reasoning chains for debugging and auditing model decision processes
Common Pitfalls
- Using chain-of-thought for simple factual lookups where it adds latency without benefit
- Trusting that explicit reasoning steps are faithful to the model's actual computation
- Not validating intermediate reasoning steps which can contain subtle errors
- Applying CoT with small models that lack the capacity for meaningful step-by-step reasoning
Advanced Tips
- Use multimodal chain-of-thought that references specific image regions or audio segments in reasoning
- Implement verification chains that check each reasoning step against retrieved evidence
- Apply chain-of-thought for complex multimodal queries like 'find videos where someone explains concept X while showing Y'
- Combine CoT with tool use to ground reasoning steps in actual computation and data retrieval