Fine-tuning
Customizing a foundation model by further training it on labeled, task-specific data.
Fine-tuning trains a foundation model further on a labeled, domain-specific dataset, updating its weights so the model internalizes new patterns rather than reading them only at inference. In Amazon Bedrock this is called model customization, available for select base models, adapting it to proprietary tone, terminology, or task formats. The key exam distinction is from retrieval-augmented generation (RAG): RAG keeps weights frozen and injects context from a knowledge base at query time, making it cheaper and easier to update. Fine-tuning bakes knowledge into the weights, which is more powerful for stylistic or behavioral change but requires labeled data, compute, and redeployment when the underlying facts change.
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