Going beyond vibes: Evaluating your Amazon Bedrock workloads for production

The article discusses moving beyond "vibe testing" for Amazon Bedrock workloads and introduces quantitative methods for evaluating foundation models (FMs) for production use.

• Amazon Bedrock offers two primary model evaluation methods:
  • Programmatic model evaluation (measuring accuracy, robustness, toxicity)
  • Model-as-judge evaluation (scoring quality and responsible AI metrics)
• Key evaluation steps include:
  • Building a ground truth dataset in .jsonl format
  • Storing the dataset in Amazon S3
  • Configuring evaluation jobs with specific models and metrics
• Benefits of quantitative evaluation:
• Objectively compare different foundation models
• Track model performance as prompts evolve
• Confidently upgrade to newer, better models

The goal is to move organizations from emotional, subjective model selection to data-driven, measurable evaluation techniques.