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

# Groundedness

> Learn how to use the groundedness test

## Definition

The groundedness test evaluates whether every factual statement in the AI assistant's response is grounded in provided context. This LLM-as-a-judge evaluation ensures that the model doesn't hallucinate information and only makes claims that are supported by the given context.

## Taxonomy

* **Task types**: LLM.
* **Availability**: <Tooltip tip="Continuously evaluate your models and datasets as you iterate on their versions.">development</Tooltip>
  and <Tooltip tip="Monitor a model in production, measure its health, check for drifts and set up alerts.">monitoring</Tooltip>.

## Why it matters

* Groundedness is crucial for RAG (Retrieval-Augmented Generation) systems where responses must be based on retrieved information.
* This metric helps prevent hallucination by ensuring that all factual claims are supported by the provided context.
* It's essential for applications where accuracy and trustworthiness are paramount, such as customer support, medical information, or legal assistance.
* Helps maintain user trust by ensuring the AI doesn't make up information that sounds plausible but is unsupported.

## Required columns

To compute this metric, your dataset must contain the following columns:

* **Outputs**: The generated response from your LLM
* **Context**: The provided context or retrieved information that should ground the response

<Info>
  To use this test, you must select the underlying LLM used as the evaluator and
  provide the required API credentials. You can check the
  [OpenAI](/integrations/openai#openai-llm-evaluator) and
  [Anthropic](/integrations/anthropic#anthropic-llm-evaluator) integration
  guides for details.
</Info>

## Evaluation criteria

The LLM evaluator assesses responses based on:

1. **Factual Statement Verification**: Does every factual statement have a clear basis in the provided context?
2. **Information Source Alignment**: Are all specific details, numbers, dates, names, and facts directly supported by the retrieved information?
3. **Hallucination Detection**: Does the response contain information that appears to be made up or not present in the context?

## Scoring guidelines

* **Score 1 (Grounded)**: All factual statements are clearly supported by the provided context
* **Score 0 (Not Grounded)**: Contains factual statements that lack clear support in the provided context

## Examples of violations

* Making specific claims about dates, numbers, or facts not mentioned in the context
* Stating opinions as facts without contextual support
* Providing specific details about people, places, or events not referenced in the context

## Examples of acceptable responses

* "Based on the provided information, \[specific fact from context]"
* "The context shows that \[directly supported claim]"
* "According to the retrieved information, \[factual statement from context]"

## Related

* [LLM-as-a-judge test](/tests/catalog/l-l-m-rubric-threshold) - Learn about custom LLM evaluation criteria.
* [Faithfulness test](/tests/catalog/faithfulness) - Measure factual consistency with context using Ragas.
* [Context utilization test](/tests/catalog/context-utilization) - Evaluate how well context is used.
* [Toxicity test](/tests/catalog/toxicity) - Detect harmful content in responses.