> ## 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

Heuristic evaluators uses mathematical and logical formulas to aproximate if a response is correct or incorrect.

## Why Heuristic Evaluators are Important

Heuristic evaluators are valuable because they:

1. **Consistency**: Provide consistent evaluations across different runs and scenarios
2. **Speed**: Execute quickly without requiring additional API calls
3. **Cost-Effective**: Don't require additional LLM API calls, making them more economical

However, there are some limitations:

* **Rigidity**: May miss nuanced or context-dependent aspects of responses
* **Limited Scope**: Can only evaluate what has been explicitly defined in the rules
* **Maintenance**: Require regular updates to handle new patterns or edge cases
* **Complexity**: May become unwieldy when trying to capture complex evaluation criteria

## Current TrustTest Heuristic Evaluators

TrustTest provides several specialized heuristic evaluators:

1. **[Regex](/trusttest/evaluate-result/heuristics/regex)**: Regular expressions against the response
2. **[Equals](/trusttest/evaluate-result/heuristics/equals)**: Exact match to an expected value
3. **[BLEU](/trusttest/evaluate-result/heuristics/bleu)**: n-gram similarity
4. **[Language](/trusttest/evaluate-result/heuristics/language)**: `ExpectedLanguageEvaluator` (`expected_languages`) and `EqualLanguageEvaluator` via FastText (`trusttest[language-detection]`)
5. **[Signature evaluators](/trusttest/evaluate-result/heuristics/signature)**: `VirusEvaluator`, `SpamEvaluator`, `PhishingEvaluator`, `XssAttackVectorEvaluator`
6. **[Bias comparison](/trusttest/evaluate-result/heuristics/bias-comparison)**: Paired Q\&A consistency (`BiasComparisonEvaluator`)

<Note>
  While heuristic evaluators are fast and consistent, we recommend using LLM as a Judge evaluators when possible as they can better understand semantic relationships and reason about content in a more human-like way.
</Note>