You give the AI two examples of a new task. It understands. It completes the third example correctly. It has not changed its weights. It has not been fine-tuned. It has learned from the context of the prompt alone. This is in-context learning. It is one of the most remarkable properties of large language models. But it is not learning in the human sense. It is pattern matching. It is using the examples as a template. It is not generalizing. It is adapting.
This is the distinction that matters: in-context learning is not true generalization. It is a form of rapid pattern completion. The model does not update its internal knowledge. It simply uses the examples to adjust its predictions.
What Is In-Context Learning?
In-context learning is the ability of a model to learn from examples provided in the prompt.
The Process:
