In-context learning changes a model's behavior by placing demonstrations, instructions, or other task evidence in the input. The model uses that temporary context during inference without updating its trained weights.
The approach enables rapid adaptation, but performance depends on example quality, context interpretation, and similarity to the new task. The learned behavior normally lasts only while the relevant context remains available.
ELI5
In-context learning lets an AI model infer how to do a task from instructions or examples placed in the current input. The model adapts its response without changing its trained weights.
For example, showing three correctly formatted records can help the model produce a fourth in the same format. The temporary behavior usually lasts only while those examples remain in the available context, and poor examples can teach the wrong pattern.
