> ## 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

> Evaluate your AI model's functional correctness and quality

Functional testing evaluates whether your AI model produces correct, relevant, and high-quality responses. Unlike threat detection which focuses on security vulnerabilities, functional testing ensures your model performs its intended tasks accurately.

## What is Functional Testing?

Functional testing validates that your AI model:

* **Answers questions correctly** based on provided context or knowledge
* **Maintains consistency** across similar queries
* **Provides relevant responses** that address user intent
* **Meets quality standards** for your specific use case

## Test Generation Methods

<CardGroup cols={3}>
  <Card title="From RAG" icon="database" href="/trusttest/create/functional/from-rag">
    Generate tests from your knowledge base
  </Card>

  <Card title="From Dataset" icon="table" href="/trusttest/create/functional/from-dataset">
    Use existing Q\&A datasets
  </Card>

  <Card title="From Prompt" icon="wand-magic-sparkles" href="/trusttest/create/functional/from-prompt">
    Generate tests dynamically with LLMs
  </Card>
</CardGroup>

***

## When to Use Functional Testing

| Use Case               | Recommended Approach                                |
| ---------------------- | --------------------------------------------------- |
| RAG applications       | From RAG - tests against your actual knowledge base |
| Customer support bots  | From Dataset - curated Q\&A pairs                   |
| General assistants     | From Prompt - dynamic test generation               |
| Domain-specific models | Combination of all approaches                       |

***

## Evaluation Methods

Functional tests can be evaluated using:

* **LLM-as-Judge**: Use an LLM to assess response quality
* **Heuristics**: Use BLEU, exact match, regex patterns
* **Custom evaluators**: Define your own evaluation logic

[Learn more about evaluation →](/trusttest/evaluate-result/overview)

***

## Quick Example

```python theme={null}
from trusttest.probes.rag import RAGProbe
from trusttest.evaluation_scenarios import EvaluationScenario
from trusttest.evaluator_suite import EvaluatorSuite
from trusttest.evaluators import AnswerRelevanceEvaluator
from trusttest.targets.http import HttpTarget, PayloadConfig

target = HttpTarget(
    url="https://your-model-endpoint.com/chat",
    headers={"Content-Type": "application/json"},
    payload_config=PayloadConfig(
        format={"messages": [{"role": "user", "content": "{{ test }}"}]},
        message_regex="{{ test }}",
    ),
)

probe = RAGProbe(
    target=target,
    knowledge_base=your_knowledge_base,
    num_questions=50,
)

scenario = EvaluationScenario(
    name="RAG Functional",
    evaluator_suite=EvaluatorSuite(evaluators=[AnswerRelevanceEvaluator()], criteria="any_fail"),
)

test_set = probe.get_test_set()
results = scenario.evaluate(test_set)
results.display_summary()
```