Inference
In-Context Learning
The ability of a language model to learn new tasks from examples or instructions provided in the prompt, without any weight updates or training.
In depth
The ability of a language model to learn new tasks from examples or instructions provided in the prompt, without any weight updates or training. When you include examples in a prompt and the model follows the pattern, that is in-context learning. It is why few-shot prompting works and why well-structured context dramatically improves model performance. The quality of in-context learning scales with model size and context window length.
Example
In practice, developers reach for In-Context Learning when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Inference.
FAQ
What is In-Context Learning?
The ability of a language model to learn new tasks from examples or instructions provided in the prompt, without any weight updates or training.
Why does In-Context Learning matter for AI developers?
In-Context Learning sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about In-Context Learning?
Developers Digest publishes tutorials and videos that cover Inference topics including In-Context Learning. Check the blog and YouTube channel for hands-on walkthroughs.
Related terms
Inference
Context Engineering
The discipline of designing what information goes into a model's context window and how it is structured.
Inference
Unsupervised Learning
A category of machine learning where models learn patterns from data without labeled examples or explicit correct answers.
Inference
Inference
The process of running input through a trained model to get a prediction or output.
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