In-Context Learning

In-context learning occurs when a language model adjusts its behavior from instructions or examples included in the current prompt without updating its stored parameters. A user can demonstrate a desired format, classification rule, tone, or reasoning pattern, and the model attempts to continue that pattern on a new input. The technique enables zero-shot, one-shot, and few-shot prompting and can be faster than fine-tuning for temporary tasks. Results are sensitive to example quality, order, wording, and the model’s context limit. Because the adaptation disappears when the context is removed, important workflows still require stable prompts, testing, version control, and safeguards against untrusted instructions.