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Use few-shot examples in your prompts to dramatically improve output quality for specific formats.

In-context learning is the ability of a large language model to learn a new task at inference time simply by being provided with a few examples in the prompt (few-shot prompting), without any updates to the model's weights. This is a powerful, emergent property of scale.

When asking an LLM to perform a task with a specific format, provide a few examples (2-3) in your prompt. This 'few-shot' approach helps the model understand the desired input-output pattern, leading to more consistent and accurate results.

Zero-shot prompting asks an LLM to perform a task without prior examples. Few-shot prompting provides 1-5 examples within the prompt, often significantly improving performance on novel tasks.

Provide a few examples (input/output pairs) in your prompt to guide the LLM's response. This is called few-shot prompting and can significantly improve performance on specific tasks.

Large language models exhibit emergent abilities that weren't explicitly trained for, such as few-shot learning, chain-of-thought reasoning, and code generation, which appear at certain model scales.