How to Train Foundation Models in Amazon Bedrock

Amazon Bedrock is a fully managed service that provides access to leading foundation models (FMs) from AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon's own Titan models. It allows businesses to build and scale generative AI applications securely and efficiently. Training or customizing these models in Bedrock can be done through various approaches such as fine-tuning, retrieval-augmented generation (RAG), and prompt engineering.

1. Fine-Tuning Foundation Models

What is Fine-Tuning?

Fine-tuning involves retraining a pre-trained model on a smaller, task-specific dataset to adapt it to your business use case. For example, you can fine-tune a Titan model to understand your organization's terminology and writing style.

How Fine-Tuning Works in Bedrock

Amazon Bedrock offers simple APIs and console workflows to fine-tune supported models like Titan Text and Titan Embeddings. You prepare a dataset in the required JSONL format containing input-output pairs, upload it to Amazon S3, and use Bedrock’s fine-tuning API to initiate the job. The service manages resource provisioning, training, and deployment of the fine-tuned model seamlessly.

2. Retrieval-Augmented Generation (RAG)

What is RAG?

RAG is a method where external knowledge sources, such as document databases, are retrieved and used alongside the model’s parameters to generate more accurate and up-to-date responses. This overcomes the limitation of model knowledge cut-off.

RAG in Amazon Bedrock

Bedrock integrates with Amazon Kendra and AWS Lambda functions to implement RAG workflows. The process involves retrieving relevant documents from a knowledge base, embedding them using models like Titan Embeddings, and feeding them with the user query into the FM to generate contextual responses. This is widely used in enterprise chatbots and virtual assistants.

3. Prompt Engineering and Agents

What is Prompt Engineering?

Prompt engineering involves crafting inputs (prompts) to guide the model towards desired outputs without any retraining. It is effective for many generative tasks like summarisation, rewriting, or classification.

Bedrock Agents

Amazon Bedrock recently introduced Agents, which combine prompt engineering, orchestration, and API calling. Agents are configured with instructions, knowledge bases, and API schemas, allowing models to interact dynamically with internal systems to execute tasks.

4. Other Customization Approaches

Apart from fine-tuning and RAG, few-shot learning is another approach where the model is provided with a few examples in the prompt itself to adapt to a task on the fly. This is useful when training data is limited, and rapid testing is required.

Conclusion

Amazon Bedrock simplifies the customization of foundation models by offering multiple techniques like fine-tuning, RAG, prompt engineering, and few-shot learning. Organizations can leverage these to build tailored, efficient, and secure generative AI applications at scale without managing underlying infrastructure complexities.