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

# Is Concise

> Evaluates whether the response is concise and to the point

<CodeGroup>
  ```python Python theme={null}
  result = evaluator.evaluate(
      eval_templates="is_concise",
      inputs={
          "output": "Honey doesn't spoil because its low moisture and high acidity prevent the growth of bacteria and other microbes."
      },
      model_name="turing_flash"
  )

  print(result.eval_results[0].output)
  print(result.eval_results[0].reason)
  ```

  ```typescript JS/TS theme={null}
  import { Evaluator, Templates } from "@future-agi/ai-evaluation";

  const evaluator = new Evaluator();

  const result = await evaluator.evaluate(
    "is_concise",
    {
      output: "Honey doesn't spoil because its low moisture and high acidity prevent the growth of bacteria and other microbes."
    },
    {
      modelName: "turing_flash",
    }
  );

  console.log(result);
  ```
</CodeGroup>

| **Input** |                    |          |                                                                |
| --------- | ------------------ | -------- | -------------------------------------------------------------- |
|           | **Required Input** | **Type** | **Description**                                                |
|           | `output`           | `string` | Generated content by the model to be evaluated for conciseness |

| **Output** |            |                                                                 |
| ---------- | ---------- | --------------------------------------------------------------- |
|            | **Field**  | **Description**                                                 |
|            | **Result** | Returns Passed if the content is concise, or Failed if it's not |
|            | **Reason** | Provides a detailed explanation of the evaluation               |

***

### Troubleshooting

If you encounter issues with this evaluation:

* Remember that conciseness depends on context - what's concise for a complex topic might still be relatively lengthy
* This evaluation works best on complete responses rather than fragments
* Very short responses may be marked as concise but might fail other evaluations like `completeness`
* Consider the balance between conciseness and adequate information - extremely brief responses might miss important details

### Related Evaluations

* [completeness](/future-agi/get-started/evaluation/builtin-evals/completeness): Ensures that despite being concise, the response addresses all aspects of a query
* [is-helpful](/future-agi/get-started/evaluation/builtin-evals/is-helpful): Evaluates if the response is actually useful despite its brevity
* [instruction-adherence](/future-agi/get-started/evaluation/builtin-evals/instruction-adherence): Checks if the response follows instructions, which might include requirements for detail
* [length-evals](/future-agi/get-started/evaluation/builtin-evals/length-evals): Provides quantitative metrics about text length