In-context learning enables AI models to understand and handle new tasks by reading examples provided within the prompt itself — without retraining the model's underlying weights. The model observes the pattern in the examples and applies it to new inputs.
It makes adapting a model to new formats, classification schemes, or task types practical and fast — without the overhead of a fine-tuning process. When a new need arises, a few well-chosen examples in the prompt often provide enough signal for the model to generalize correctly. This is part of what makes modern LLMs so flexible in enterprise settings.
