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Regularly check model provider documentation for updated tokenization rules and pricing.

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.