Amazon Bedrock Evaluations: Custom Metrics for Generative AI
Amazon Bedrock Evaluations now allows users to create custom metrics for evaluating generative AI applications, extending its LLM-as-a-judge capabilities. This empowers organizations to tailor evaluations to specific business needs, moving beyond pre-built metrics like correctness, completeness, and faithfulness. Both quantitative (numerical) and qualitative (categorical) scoring is supported, enabling the creation of ordinal and nominal metrics or even classification tasks. The system offers a simplified experience with pre-built templates and the ability to create metrics from scratch. Custom metrics can be saved for reuse and imported/exported via JSON. Dynamic content integration is enabled through template variables like {{prompt}}, {{prediction}}, and {{context}}. The output is customizable, with a recommended schema for consistency. The article details how to create evaluation jobs with custom metrics using both the AWS Management Console and the Python SDK. For model evaluation, the JSONL format for input data is specified, highlighting optional fields like referenceResponse and category. RAG evaluation’s JSONL format is also detailed, showcasing the flexibility introduced by the new referenceContexts field. The process involves defining the custom metric’s name, instructions (including template variables), and rating scale (numerical or string). The article provides examples of both numerical and string rating scales, as well as a definition with no scale. The article further covers best practices and considerations for using custom metrics, emphasizing the importance of using the built-in output schema for optimal results. While the examples focus on Amazon Bedrock models, the BYOI (bring your own inference) feature allows evaluation of models hosted elsewhere. The Python SDK examples demonstrate how to configure custom metrics within the create_evaluation_job API call. This feature significantly enhances the control and precision of generative AI evaluations, aligning them with specific business requirements. A key limitation is that custom metrics are currently only available for LLM-as-a-judge, not custom AWS Lambda functions or endpoints.
Amazon’s ai automation bedrock provides developers with powerful evaluation tools to measure and improve the performance of their generative AI applications.
While many organizations rely on chatgpt automation metrics for basic performance tracking, Amazon Bedrock Evaluations offers more sophisticated custom measurement capabilities for enterprise AI applications.
