"In-context learning" in the realm of Large Language Models (LLMs) refers to the ability of these
models to learn and adapt to a specific task by being provided with a few examples of that task
within the input prompt. This approach allows the model to understand the desired pattern or
structure from the given examples and apply it to generate the correct outputs for new, similar
inputs. In-context learning is powerful because it does not require retraining the model; instead, it
uses the examples provided within the context of the interaction to guide its behavior.
