Evaluating Generative AI with Amazon Bedrock Model Evaluation

Introduction

Evaluating foundation models is essential to ensure their reliability, effectiveness, and alignment with business goals. As generative AI becomes central to applications across industries, ranging from chatbots and content generation to data summarization and customer service, assessing the output of these models becomes increasingly critical.

Amazon Bedrock, a fully managed service to build and scale generative AI applications, simplifies this process by offering built-in model evaluation capabilities. Whether you’re testing Anthropic Claude, AI21 Labs’ Jurassic, Meta’s Llama, or Amazon Titan, Bedrock provides both automatic and human evaluation methods to help assess the model’s quality. This blog walks you through the evaluation process and how it empowers teams to make data-driven decisions.

Importance of Model Evaluation

Before deploying a foundation model into production, it’s important to assess its accuracy, coherence, and safety. Poorly performing models can lead to:

• Misinformation in generated outputs
• Inconsistent or irrelevant responses
• Biases that may affect fairness or inclusion
• Security or reputational risks if inappropriate content is produced

By evaluating a model early, organizations can uncover such issues before they impact users. For example, in a customer support use case, inaccurate responses could lead to customer dissatisfaction or even legal concerns. Proper evaluation also ensures that the model is fit for purpose, whether you’re using it for summarization, code generation, or question-answering.

Amazon Bedrock offers two robust approaches to model evaluation

Automatic Evaluation

Automatic evaluation in Bedrock enables users to assess model responses using pre-set metrics and datasets without requiring manual intervention. There are two key modes:

• Programmatic Evaluation

Programmatic evaluation allows users to test a model using input-output pairs. These pairs can be from a predefined dataset or a custom dataset. You define the metrics, such as BLEU (for text similarity), ROUGE (for summarization), or toxicity scores, and Bedrock automatically evaluates the model’s responses at scale. This is ideal for teams looking to test many prompts quickly and objectively.

Example: If you’re building a product description generator, you can input sample product titles and compare generated descriptions against reference descriptions using similarity scores.

• Model as a Judge

This method leverages another foundation model, like Claude or Titan, to assess the outputs of your primary model. You give it evaluation criteria (e.g., relevance, correctness, tone), and the evaluator model scores the outputs accordingly. This helps in evaluating nuanced aspects of a response that traditional metrics might miss.

Example: In a chatbot scenario, you can ask the model-as-judge to rate answers based on helpfulness, appropriateness, and empathy.

Human Evaluation

Even with the most advanced automation, human judgment remains essential in evaluating nuanced or subjective aspects of language. Bedrock supports two modes of human evaluation:

• AWS Managed Work Team

Amazon provides a curated team of human reviewers who evaluate up to two different models’ outputs. You define the evaluation rubric (e.g., factuality, fluency, tone), and reviewers assess which model performs better across prompts. This is especially useful when you’re deciding between two providers, like Claude vs. Titan.

Use Case: Comparing how two models summarize medical articles for accuracy and readability.

• Bring Your Own Work Team

If you want internal experts or business stakeholders to handle the evaluation, Bedrock enables you to assign the task to your own workforce. You can also define job-specific evaluation forms and standards, ensuring the reviewers focus on what matters most to your use case.

Use Case: A legal team evaluates generative model outputs to ensure contract summaries maintain key legal clauses and intent.

Reviewing and Analyzing Results

Evaluation is not complete without proper analysis. Amazon Bedrock automatically stores evaluation results in Amazon S3, allowing users to track and compare model performance over time.

Key Steps After Evaluation:

• Visualize Results: Use tools like Amazon QuickSight or SageMaker Studio to analyze performance metrics and evaluation feedback.
• Detect Issues: Spot patterns in bias, hallucination, or low coherence in model responses.
• Refine Your Prompts: Prompt engineering may improve results without switching models.
• Experiment with Models: If one model underperforms, switch to an alternative provider in Bedrock with minimal code changes.
• Create a Feedback Loop: Feed evaluation data back into your model improvement or prompt tuning process.

Tip: Track metrics like model score variance across use cases (e.g., customer service vs. technical support) to decide on specialization.

Conclusion

Amazon Bedrock provides a flexible, scalable, and comprehensive solution for evaluating generative AI models. Whether you’re running automated tests on hundreds of prompts or collecting insights from human reviewers, Bedrock’s evaluation tools help ensure your models are accurate, fair, and aligned with your business goals.

By integrating both quantitative metrics and qualitative human input, you can confidently select, fine-tune, and deploy foundation models that perform consistently across real-world applications. Understanding this evaluation process is essential to delivering generative AI applications that are not only powerful but also responsible and trustworthy.

References

• Amazon Bedrock – Foundation Model Evaluation
• Amazon Bedrock Model Evaluation Blog
• What is Amazon Bedrock? – AWS

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