> ## 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

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 Evaluator**: Uses regular expressions to validate response patterns
2. **Equals Evaluator**: Checks if responses exactly match expected values
3. **BLEU Evaluator**: Measures the similarity between responses using the BLEU score metric
4. **Expected Language Evaluator**: Verifies if responses are in the expected language
5. **Equal Language Evaluator**: Compares the language of responses to ensure consistency

<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>