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, rather than supplying it at inference time through prompts or retrieved context. With these approaches, you get faster inference, lower token costs, and higher accuracy on the tasks that matter most to your business. Amazon Bedrock manages the training process automatically, requiring only that you upload your data to Amazon Simple Storage Service (Amazon S3) and initiate the job through the AWS Management Console, CLI, or API. Deep machine learning expertise is not required. Nova models support on-demand invocation of customized models in Amazon Bedrock. This means you pay only per-call at the standard rate for the model, instead of needing to purchase more expensive allocated capacity (Provisioned Throughput).

In this post, we’ll walk you through a complete implementation of model fine-tuning in Amazon Bedrock using Amazon Nova models, demonstrating each step through an intent classifier example that achieves superior performance on a domain specific task. Throughout this guide, you’ll learn to prepare high-quality training data that drives meaningful model improvements, configure hyperparameters to optimize learning without overfitting, and deploy your fine-tuned model for improved accuracy and reduced latency. We’ll show you how to evaluate your results using training metrics and loss curves.

Context-engineering techniques such as prompt engineering or Retrieval-Augmented Generation (RAG) place information into the model’s prompt. These approaches offer significant advantages: they take effect immediately with no training required, allow for dynamic information updates, and work with multiple foundation models without modification. However, these techniques consume context window tokens on every invocation, which can increase cumulative costs and latency over time. More importantly, they do not generalize well. The model is simply reading instructions each time rather than having internalized the knowledge, so it can struggle with novel phrasings, edge cases, or tasks that require reasoning beyond what was explicitly provided in the prompt. Customization techniques, by comparison, incorporate the new knowledge directly into the model by adding an adapter matrix of additional weights and customizing those (“parameter-efficient fine-tuning”, aka “PEFT”). The resulting customized model has acquired new domain-specific skills. Customization allows faster and more efficient small models to reach performance comparable to larger models in the specific training domain.

When to fine-tune: Consider fine-tuning when you have a high-volume, well-defined task where you can assemble quality labeled examples or a reward function. Use cases include training a model to correctly render your company’s logo, embedding brand tone and company policies into the model, or replacing a traditional ML classifier with a small LLM. For example, Amazon Customer Service customized Nova Micro for specialized customer support to improve accuracy and reduce latency, improving accuracy by 5.4% on domain-specific issues and 7.3% on general issues.

Fine-tuned small LLMs like Nova Micro are increasingly replacing traditional ML classifiers for tasks such as intent detection. They deliver the flexibility and world knowledge of an LLM at the speed and cost of a lightweight model. Unlike classifiers, LLMs handle natural variation in phrasing, slang, and context without retraining, and fine-tuning sharpens their accuracy further for the specific task. We demonstrate this with an intent classifier example later in this blog.