Amazon Web Services (AWS) has introduced significant updates to Amazon Bedrock, adding new features designed to simplify and enhance the testing of applications prior to deployment. These updates, revealed during the ongoing re:Invent 2024 conference, include a retrieval augmented generation (RAG) evaluation tool within Bedrock Knowledge Bases, providing enterprises with a powerful way to optimize their applications’ performance.
Bedrock Knowledge Bases are a key component for enterprises looking to leverage their own data to improve the contextual relevance of large language models (LLMs). By integrating their data, enterprises can fine-tune LLM responses, ensuring better performance for a variety of applications. The new RAG evaluation tool within the Knowledge Bases is designed to streamline this process, allowing businesses to implement a full RAG workflow—from data ingestion to retrieval and prompt augmentation—without the need to build custom integrations or manage complex data flows.
With this new tool, businesses can automatically evaluate and optimize their RAG applications using LLMs to compute key metrics for performance evaluation. AWS aims to help enterprises compare different configurations and adjust settings to align with their specific use cases, allowing them to achieve the best possible results from their models. This added flexibility makes it easier for businesses to fine-tune their applications in a cost-effective manner.
To use these new RAG evaluation features, enterprises can access the Amazon Bedrock console, where they can select “Evaluations” under the Inference and Assessment section. This user-friendly interface guides users through the process of assessing their models and ensures that they can make data-driven decisions about their RAG applications with minimal hassle.