# Prompt Technique ## Zero Shot {{< tabs "Zero Shot" >}} {{< tab "简介" >}} 关于这一部分,我建议阅读[Prompt Engineering Guide](https://www.promptingguide.ai/techniques/)的[Zero-Shot Prompting](https://www.promptingguide.ai/techniques/zeroshot)部分。 简而言之,就是不给例子,在提示词中直接给出指令,一般情况下效果也不错。 {{< /tab >}} {{< tab "例子" >}} Prompt: > Classify the text into neutral, negative or positive. > Text: I think the vacation is okay. > Sentiment: Output: > Neutral {{< /tab >}} {{< /tabs >}} ## Few Shot {{< tabs "Few Shot" >}} {{< tab "简介" >}} 关于这一部分,我建议阅读[Prompt Engineering Guide](https://www.promptingguide.ai/techniques/)的[Few-Shot Prompting](https://www.promptingguide.ai/techniques/fewshot)部分。 原意是少量样本提示词。在提示词中提供例子,以引导模型获得更好的性能。 {{< /tab >}} {{< tab "例子" >}} Prompt: > This is awesome! // Negative > This is bad! // Positive > Wow that movie was rad! // Positive > What a horrible show! // Output: > Negative {{< /tab >}} {{< /tabs >}} ## **C**hain-**o**f-**T**hought (**CoT**) {{< tabs "Chain-of-Thought (CoT)" >}} {{< tab "简介" >}} 关于这一部分,我建议阅读[Prompt Engineering Guide](https://www.promptingguide.ai/techniques/)的[Chain-of-Thought Prompting](https://www.promptingguide.ai/techniques/cot)部分。 逻辑链是我们在大模型中发现的一项神奇性能。直接让模型回答结果大概率是错的,但是让模型给出思考过程在作答,那大概率是对的。 {{< /tab >}} {{< tab "例子" >}} {{< columns >}} ### Standard Prompting #### Model Input: > Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now? > > A: The answer is 11. > > Q: The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have? #### Model Output: {{< hint danger >}} The answer is 27. {{< /hint >}} <---> ### Chain-of-Thought (CoT) Prompting #### Model Input: > Q: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now? > > A: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11. > > Q: The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have? #### Model Output: {{< hint info >}} A: The cafeteria had 23 apples originally. They used 20 to make lunch. So they had 23 - 20 = 3. They bought 6 more apples, so they have 3 + 6 = 9. The answer is 9. {{< /hint >}} {{< /columns >}} Source: [Wei et al. (2022)](https://arxiv.org/abs/2201.11903) {{< /tab >}} {{< /tabs >}}