Also known as: Prompt based Learning
Prompt-based learning is a model capability or usage pattern in which behavior is adapted through natural-language prompts rather than direct retraining. The model uses its pre-existing knowledge and instruction-following behavior to respond to new tasks, and in some systems user feedback can further guide future interactions.
A capability of certain models that enables them to adapt their behavior in response to arbitrary text input (prompts). In a typical prompt-based learning paradigm, a large language model responds to a prompt by generating text. For example, suppose a user enters the following prompt: Summarize Newton's Third Law of Motion. A model capable of prompt-based learning isn't specifically trained to answer the previous prompt. Rather, the model "knows" a lot of facts about physics, a lot about general language rules, and a lot about what constitutes generally useful answers. That knowledge is sufficient to provide a (hopefully) useful answer. Additional human feedback ("That answer was too complicated." or "What's a reaction?") enables some prompt-based learning systems to gradually improve the usefulness of their answers.