Chain-of-Thought
Prompting a model to show step-by-step reasoning before its final answer.
Chain-of-thought (CoT) prompting instructs a foundation model to produce explicit intermediate reasoning steps before its final answer. By surfacing the model’s “work,” CoT improves accuracy on multi-step logic, arithmetic, and causal problems where direct-answer prompts tend to skip steps. On AIF-C01, distinguish zero-shot CoT (adding “think step by step,” no examples needed) from few-shot CoT (including worked reasoning chains). Both are prompt-engineering techniques usable with any model invoked through Amazon Bedrock. CoT is not fine-tuning, RAG, or retrieval augmentation — confusing it with those is a common exam trap.
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