> ## Documentation Index
> Fetch the complete documentation index at: https://neuraltrust-92b43583-develop.mintlify.site/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** — rule-based, no extra LLM call:

* [Language](/trusttest/evaluate-result/heuristics/language) — FastText (`expected_languages`)
* [Equals](/trusttest/evaluate-result/heuristics/equals), [Regex](/trusttest/evaluate-result/heuristics/regex), [BLEU](/trusttest/evaluate-result/heuristics/bleu)
* [Signature evaluators](/trusttest/evaluate-result/heuristics/signature) — Virus, Spam, Phishing, XSS
* [Bias comparison](/trusttest/evaluate-result/heuristics/bias-comparison)

**LLM judges**:

* [Correctness](/trusttest/evaluate-result/llm-as-a-judge/correctness), [Completeness](/trusttest/evaluate-result/llm-as-a-judge/completeness), [Tone](/trusttest/evaluate-result/llm-as-a-judge/tone)
* [URL correctness](/trusttest/evaluate-result/llm-as-a-judge/url-correctness), [True/False](/trusttest/evaluate-result/llm-as-a-judge/true-false)
* [Answer relevance](/trusttest/evaluate-result/llm-as-a-judge/answer-relevance), [RAG poisoning](/trusttest/evaluate-result/llm-as-a-judge/rag-poisoning)
* [Custom](/trusttest/evaluate-result/llm-as-a-judge/custom) — `CustomEvaluatorExpected` / `CustomEvaluatorObjective`

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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.