Improve RAG accuracy with fine-tuned embedding models on Amazon SageMaker

This article discusses how to improve the accuracy of Retrieval Augmented Generation (RAG) models by fine-tuning embedding models on domain-specific data using Amazon SageMaker.

Specifically, the article covers:

• An overview of RAG models and the challenges of using pre-trained embedding models for domain-specific tasks
• The importance of fine-tuning embedding models on domain-specific data to capture relevant semantics and context
• Step-by-step instructions for fine-tuning a Sentence Transformer embedding model on Amazon Bedrock FAQs using SageMaker
• Deployment of the fine-tuned embedding model as a SageMaker endpoint for inference
• A comparison of the fine-tuned model's performance with the pre-trained model, demonstrating improved accuracy in capturing semantic relationships
• Conclusion highlighting the benefits of fine-tuning embeddings for RAG models in specialized domains