Glossary · Prompting & context
In-Context Learning
A model adapting its behavior from instructions, examples, or patterns supplied in the current input without an ordinary parameter update.
Why it matters
It explains how one pretrained model can perform a new task from context while keeping its weights unchanged.
In practice
Place representative demonstrations before the target input, test order and formatting variants, and keep evaluation examples separate from the demonstrations.
Common confusion
In-context learning is temporary conditioning, not fine-tuning, durable memory, or proof that the model inferred the intended rule.
Related terms
Sources
Browse the learning paths to see this term in context — every lesson is free to read.