In-Context Learning
Guiding a model's behavior by including instructions or examples directly in the prompt.
In-context learning shapes a foundation model’s behavior entirely through the text supplied in the prompt — examples, instructions, or role descriptions — without updating any model parameters. Because the weights stay frozen, no training job or fine-tuning pipeline is needed, making it the fastest way to steer a model in Amazon Bedrock. The key exam distinction is between in-context learning and fine-tuning: fine-tuning permanently adjusts model weights using a training dataset, while in-context learning is temporary and prompt-scoped. Zero-shot, one-shot, and few-shot prompting are all forms of in-context learning, differing only in how many examples the prompt embeds.
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