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

# Conciseness

> Evaluator to assess how concise and to-the-point an answer is.

Evaluator to assess how concise and to-the-point an answer is.

The Conciseness evaluator measures whether an LLM's answer is appropriately
brief and direct without unnecessary verbosity. This metric is important for
ensuring that responses are efficient and don't waste users' time with
irrelevant details or excessive elaboration.

Key Features:

* **Conciseness Assessment**: Determines if the answer is appropriately brief
* **Binary Scoring**: Returns 1.0 for concise answers, 0.0 for verbose ones
* **Detailed Reasoning**: Provides explanation for the conciseness assessment
* **Fiddler API Integration**: Uses Fiddler's built-in conciseness evaluation model

Use Cases:

* **Customer Support**: Ensuring responses are direct and helpful
* **Technical Documentation**: Verifying explanations are clear and brief
* **Educational Content**: Checking if explanations are appropriately detailed
* **API Responses**: Ensuring responses are efficient and focused

Scoring Logic:

* **1.0 (Concise)**: Answer is appropriately brief and to-the-point
* **0.0 (Verbose)**: Answer is unnecessarily long or contains irrelevant details

## Parameters

* **response** (*str*) – The LLM's response to evaluate for conciseness.
* **model** (*str*)
* **credential** (*str* *|* *None*)
* **kwargs** (*Any*)

## Returns

<ResponseField type="Score">
  A Score object containing:

  * value: 1.0 if concise, 0.0 if verbose
  * label: String representation of the boolean result
  * reasoning: Detailed explanation of the assessment
</ResponseField>

## Example

```python theme={null}
from fiddler_evals.evaluators import Conciseness
evaluator = Conciseness()
```

```python theme={null}
# Concise answer
score = evaluator.score("The capital of France is Paris.")
print(f"Conciseness: {score.value}")  # 1.0
print(f"Reasoning: {score.reasoning}")

# Verbose answer
score = evaluator.score(

    "Well, that's a great question about France. Let me think about this..."
    "France is a beautiful country in Europe, and it has many wonderful cities..."
    "The capital city of France is Paris, which is located in the north-central part..."

)
print(f"Conciseness: {score.value}")  # 0.0
```

<Info>
  This evaluator uses Fiddler's built-in conciseness assessment model
  and requires an active connection to the Fiddler API.
</Info>

## name *= 'conciseness'*

## score()

Score the conciseness of an answer.

### Parameters

<ParamField path="response" type="str" required={true}>
  The LLM's response to evaluate for conciseness.
</ParamField>

### Returns

<ResponseField type="Score">
  A Score object containing:

  * value: 1.0 if concise, 0.0 if verbose
  * label: String representation of the boolean result
  * reasoning: Detailed explanation of the assessment
</ResponseField>