Custom Evaluators

Define your quality standards with precision. Custom evaluators let you create evaluation systems tailored to your specific business requirements, ensuring your AI meets the exact quality criteria that matter most to your users.

Transform generic quality assessment into domain-specific evaluation that understands your unique requirements and standards.

Why Custom Evaluators Matter

While Handit provides powerful general-purpose evaluators, every AI application has unique quality requirements. A medical AI needs to assess clinical accuracy, a customer service AI must evaluate empathy and resolution effectiveness, and an educational AI should focus on clarity and pedagogical value.

Custom evaluators bridge this gap by allowing you to define evaluation criteria that align with your specific domain, business objectives, and user expectations.

Getting Started

Creating custom evaluators in Handit is straightforward and follows a clear pattern:

1. Define your criteria - Specify what quality means for your use case
2. Configure evaluation logic - Set up the assessment rules and scoring
3. Test and refine - Validate your evaluator with sample data
4. Deploy and monitor - Integrate into your AI pipeline

Basic Custom Evaluator Structure

Here’s a simple example of a custom evaluator:

from handit import CustomEvaluator
 
class AccuracyEvaluator(CustomEvaluator):
    def evaluate(self, response, expected):
        # Your custom evaluation logic here
        return {
            "score": 0.85,
            "reasoning": "Response is factually accurate and complete"
        }

This is a simplified version of the custom evaluators documentation.