In-Context Learning (ICL)

In-Context Learning (ICL) is the ability of large language models to solve new tasks directly from examples provided in the input prompt — without updating model weights and without traditional training. The model infers the task's pattern from the provided examples and applies that logic to the actual query. The mechanism operates through prompt structure: when input-output pairs (called shots) are prepended to the prompt, the model implicitly learns the task format and expected output logic. Zero-shot ICL requires no examples at all; few-shot ICL typically provides two to eight demonstrations. ICL is a defining capability of modern foundation models: it enables flexible adaptation to new tasks without expensive fine-tuning. For organizations, this means that many use cases — from classification and extraction to translation and summarization — can be solved through carefully designed prompts alone. The quality and representativeness of the in-prompt examples directly determines output accuracy.