> ## Documentation Index
> Fetch the complete documentation index at: https://docs.neuraltrust.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

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.