Zero-Shot Prompting | Prompt Engineering Guide
Large language models (LLMs) today, such as GPT-3.5 Turbo, GPT-4, and Claude 3, are tuned to follow instructions and are trained on large amounts of data. Large-scale training makes these models capable of performing some tasks in a "zero-shot" manner. Zero-shot prompting means that the prompt used to interact with the model won't contain examples or demonstrations. The zero-shot prompt directly instructs the model to perform a task without any additional examples to steer it. We tried a few zero-shot examples in the previous section. Here is one of the examples (ie., text classification) we used: Prompt: Output: Note that in the prompt above we didn't provide the model with any examples of text alongside their classifications, the LLM already understands "sentiment" -- that's the zero-shot capabilities at work. Instruction tuning has been shown to improve zero-shot learning Wei et al. (2022) (opens in a new tab) . Instruction tuning is essentially the concept of finetuning models on d
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