Amazon Bedrock Evaluations: Custom Metrics for Generative AI
Amazon Bedrock Evaluations now allows users to create custom metrics for evaluating generative AI applications. This enhancement extends the existing LLM-as-a-judge framework, enabling more precise and tailored assessments beyond the built-in metrics like correctness, completeness, and faithfulness. The target audience includes businesses and developers using generative AI models, whether hosted on Amazon Bedrock or other platforms (BYOI). Key features include simplified setup with pre-built templates, support for both numerical and categorical scoring, streamlined workflow management for reusing custom metrics, and dynamic content integration using variables like {{prompt}} and {{prediction}}. The system supports both model and RAG (Retrieval Augmented Generation) evaluations. For model evaluation, the input dataset uses a JSONL format specifying prompt, reference response, and model responses. RAG evaluation allows users to provide reference contexts, enabling comparison of retrieved passages to expected ones. The system offers flexibility in output control, allowing users to define custom output formats for specialized use cases. Technical specifications include the use of JSON to define custom metrics, with examples provided for numerical and string scales, and the use of template variables to inject data into evaluation prompts. While the system offers extensive customization, one limitation is that custom metrics are currently only supported for LLM-as-a-judge evaluations; custom AWS Lambda functions or endpoints are not yet supported. In summary, Amazon Bedrock Evaluations' custom metrics provide a powerful tool for organizations to align AI system evaluations with their specific business requirements, leading to more actionable insights and improved AI performance.
Custom ai automation metrics in Amazon Bedrock enable developers to measure and optimize the performance of their generative AI applications effectively.
Organizations seeking alternatives to chatgpt automation bedrock provides a comprehensive evaluation framework for measuring generative AI model performance and quality.
