Model Evaluations (Evals)

Introduction

As AI applications grow in complexity, ensuring the quality and consistency of model outputs becomes a critical challenge. We are excited to introduce Evals, a comprehensive evaluation framework designed to help you systematically test, benchmark, and optimize your prompts and model configurations. This feature eliminates guesswork, allowing you to deploy updates with confidence based on concrete performance data.

Feature Highlights

• Automated Test Suites: Run your prompts against a defined set of test cases to quickly identify regressions or improvements.
• Custom Evaluation Criteria: Define specific metrics, such as accuracy, tone, or formatting, to grade model responses automatically.
• Comparative Analytics: Compare performance across different model versions, prompt variations, or LLM providers side-by-side.

How to Use

1. Navigate to the Evals section in your dashboard sidebar.
2. Click Create New Eval and define your test dataset, including input prompts and expected ground-truth outputs.
3. Select the models or prompt templates you want to test, configure your grading criteria, and click Run Evaluation to view the detailed performance report.

Benefits

• Higher Output Quality: Ensure your AI applications consistently deliver accurate and reliable responses.
• Faster Iteration: Rapidly test new prompts and models without manual verification or fear of breaking existing behavior.
• Data-Driven Decisions: Use clear, quantifiable metrics to choose the best-performing model configurations for your use case.

Example Workflow

Imagine you are building a customer support bot and want to switch to a new LLM provider. Instead of manually testing dozens of prompts, you upload a dataset of 50 common customer queries to the Evals dashboard. You run the evaluation on both models using a semantic similarity grader. The dashboard reveals that the new model improves accuracy by 15% while maintaining the correct brand tone, giving you the empirical green light to upgrade your production environment.

Result

With the new Evals framework, you can transition from subjective prompt engineering to rigorous, data-driven AI development. By automating the testing and benchmarking process, you save time, reduce errors, and deliver superior AI experiences to your users.