Define In-Context Learning?

In-context learning is a model's ability to pick up a new pattern or task just from examples given directly in the prompt, without any additional training or parameter updates.

• The model isn't actually "learning" in the traditional sense, its weights don't change
• Instead, it recognizes the pattern demonstrated in the prompt and continues it for the new input
• Works because the model's pretraining exposed it to an enormous variety of patterns and structures it can recognize and mimic

This capability is what makes prompting techniques like few-shot examples effective, you're not training the model, you're showing it exactly what kind of response you want within the conversation itself.

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