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The model's apparent ability to learn a new task from examples provided in the prompt, without any gradient updates. Researchers debate whether this is true learning or sophisticated pattern matching, but the practical result is that large language models can be specialized at inference time with carefully chosen context.
In-context learning is the reason RAG and few-shot prompting work. AI engineering teams build around it.
The model's apparent ability to learn a new task from examples provided in the prompt, without any gradient updates. Researchers debate whether this is true learning or sophisticated pattern matching, but the practical result is that large language models can be specialized at inference time with carefully chosen context.
In-context learning is the reason RAG and few-shot prompting work. AI engineering teams build around it.
Definitions are original explanations written for career development purposes. For authoritative technical definitions, refer to NIST, ISO, or the relevant standards body.
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