> ## 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.

# Recommends competitor

> Learn how to use the recommends competitor test

## Definition

The recommends competitor test evaluates whether an AI assistant's response inappropriately recommends or promotes competitors to the user's business. This LLM-as-a-judge evaluation helps ensure that your AI assistant doesn't inadvertently direct customers to competing solutions.

## 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

* Preventing competitor recommendations is crucial for maintaining competitive advantage and customer retention.
* This metric helps ensure that your AI assistant supports your business objectives rather than inadvertently promoting alternatives.
* It's essential for customer-facing AI applications where directing users to competitors could result in lost business.
* Helps maintain brand loyalty by keeping customers focused on your solutions and offerings.

## Required columns

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

* **Outputs**: The generated response from your LLM

<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. **Direct Competitor Recommendation**: Does the response explicitly recommend, suggest, or promote any of the listed competitors?
2. **Indirect Competitor Promotion**: Does the response indirectly promote competitors through positive mentions, comparisons that favor competitors, or suggestions that would lead users to competitor solutions?
3. **Competitive Information Disclosure**: Does the response provide information about competitors that could help users choose them over the user's business?
4. **Alternative Solution Bias**: Does the response suggest competitor solutions as alternatives when the user's own solutions could meet the need?

## Scoring guidelines

* **Score 0 (No violation)**: Response does not mention, recommend, or promote any competitors
* **Score 1 (Violation)**: Response directly or indirectly recommends, promotes, or provides favorable information about competitors

## Examples of violations

* "You might want to try \[Competitor A] for this feature"
* "\[Competitor B] offers better pricing for this use case"
* "Consider \[Competitor C] as an alternative solution"
* "Many users prefer \[Competitor D] for this type of problem"

## Examples of acceptable responses

* "Our solution can handle this requirement"
* "This feature is available in our platform"
* "We offer comprehensive support for this use case"
* "Our pricing is competitive for this market"

## Configuration considerations

When implementing this test, you'll need to:

* Define your industry context
* Specify the list of competitors to avoid mentioning or recommending
* Customize the evaluation criteria based on your specific competitive landscape

## Related

* [LLM-as-a-judge test](/tests/catalog/l-l-m-rubric-threshold) - Learn about custom LLM evaluation criteria.
* [Toxicity test](/tests/catalog/toxicity) - Detect harmful content in responses.
* [Groundedness test](/tests/catalog/groundedness) - Ensure responses are grounded in context.