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
Common questions
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
Related terms
Inference
Context Engineering
The discipline of designing what information goes into a model's context window and how it is structured.
Inference
Pretraining
The first and most expensive stage of building a language model, where it learns to predict the next token across a huge unlabeled corpus of web text, books, and code.
Inference
Context Rot
The decline in LLM output quality as the number of input tokens grows, even on simple tasks that fit well inside the context window.
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