Prompt engineering for text generation: Difference between revisions

[[Chain of Thought Prompting]] (CoT prompting) is a technique introduced by Wei et al. (2022) to generate a sequence of short sentences describing step-by-step reasoning, known as [[reasoning chains]] or [[rationales]], leading to the final answer. [[CoT prompting]] is particularly useful for complex reasoning tasks when applied to large language models (e.g., those with over 50 billion parameters), while simpler tasks may benefit only marginally.

[[Few-shot CoT prompting]] involves providing the model with a limited number of demonstrations, each containing either manually written or model-generated high-quality reasoning chains. Examples of such demonstrations are provided in the original article, showcasing how this type of prompting is used to solve various mathematical reasoning problems.

[[Zero-shot CoT prompting]] uses natural language statements, such as "Let's think step by step" or "Let's work this out step by step to be sure we have the right answer," to explicitly encourage the model to generate reasoning chains. Following this, a statement like "Therefore, the answer is" is used to prompt the model to produce the final answer (Kojima et al. 2022; Zhou et al. 2022).