Customize Amazon Nova models with Amazon Bedrock fine-tuning

Today, we’re sharing how Amazon Bedrock makes it straightforward to customize Amazon Nova models for your specific business needs. As customers scale their AI deployments, they need models that reflect proprietary knowledge and workflows — whether that means maintaining a consistent brand voice in customer communications, handling complex industry-specific workflows or accurately classifying intents in a high-volume airline reservation system. Techniques like prompt engineering and Retrieval-Augmented Generation (RAG) provide the model with additional context to improve task performance, but these techniques do not instill native understanding into the model.

Amazon Bedrock supports three customization approaches for Nova models: supervised fine-tuning (SFT), which trains the model on labeled input-output examples; reinforcement fine-tuning (RFT), which uses a reward function to guide learning toward target behaviors; and model distillation, which transfers knowledge from a larger teacher model into a smaller, faster student model. Each technique embeds new knowledge directly into the model weights ...