In TrustTest there are specialized components designed to assess if an AI model response is compliant with a specific set of criteria. They provide a systematic way to measure various aspects of model outputs against predefined criteria, ensuring reliable and consistent evaluation across different use cases.

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Key Areas

Heuristic Evaluators These evaluators use rule-based approaches and predefined metrics to assess responses. They include:

• Language-based evaluations ( checks if the response is in the correct language)
• Exact matching and pattern recognition
• BLEU score for text similarity
• Regular expression pattern matching

LLM-based Evaluators These evaluators leverage language models to perform more nuanced assessments:

• Response correctness
• Response completeness
• Tone and style analysis
• URL correctness validation
• Custom evaluation criteria
• True/false assessment

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Why It Matters

• Quality Assurance Evaluators provide objective metrics to ensure AI responses meet quality standards and requirements.

• Consistent Assessment By standardizing evaluation criteria, evaluators enable reproducible and comparable results across different models and use cases.

• Flexible Evaluation The modular design allows for custom evaluators to be created for specific needs while maintaining a consistent interface.

• Comprehensive Analysis Different types of evaluators can be combined to provide a holistic assessment of model performance across multiple dimensions.

• Trust and Reliability Systematic evaluation helps build confidence in AI systems by providing clear metrics and explanations for assessment results.