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

# Call Analysis

> At the end of the call, you can summarize and evaluate how it went.

The Call Analysis feature allows you to summarize and evaluate calls, providing valuable insights into their effectiveness. This feature uses a combination of prompts and schemas to generate structured data and success evaluations based on the call's content.

You can customize the below in the assistant's `assistant.analysisPlan`.

## Summary Prompt

The summary prompt is used to create a concise summary of the call. This summary is stored in `call.analysis.summary`.

### Default Summary Prompt

The default summary prompt is:

```text
You are an expert note-taker. You will be given a transcript of a call. Summarize the call in 2-3 sentences, if applicable.
```

### Customizing the Summary Prompt

You can customize the summary prompt by setting the `summaryPrompt` property in the API or SDK:

```json
{
  "summaryPrompt": "Custom summary prompt text"
}
```

To disable the summary prompt, set it to an empty string `""` or `"off"`:

```json
{
  "summaryPrompt": ""
}
```

## Structured Data Prompt

The structured data prompt extracts specific pieces of data from the call. This data is stored in `call.analysis.structuredData`.

### Default Structured Data Prompt

The default structured data prompt is:

```text
You are an expert data extractor. You will be given a transcript of a call. Extract structured data per the JSON Schema.
```

### Customizing the Structured Data Prompt

You can set a custom structured data prompt using the `structuredDataPrompt` property:

```json
{
  "structuredDataPrompt": "Custom structured data prompt text"
}
```

## Structured Data Schema

The structured data schema enforces the format of the extracted data. It is defined using JSON Schema standards.

### Customizing the Structured Data Schema

You can set a custom structured data schema using the `structuredDataSchema` property:

```json
{
  "structuredDataSchema": {
    "type": "object",
    "properties": {
      "field1": { "type": "string" },
      "field2": { "type": "number" }
    },
    "required": ["field1", "field2"]
  }
}
```

## Success Evaluation Prompt

The success evaluation prompt is used to determine if the call was successful. This evaluation is stored in `call.analysis.successEvaluation`.

### Default Success Evaluation Prompt

The default success evaluation prompt is:

```text
You are an expert call evaluator. You will be given a transcript of a call and the system prompt of the AI participant. Determine if the call was successful based on the objectives inferred from the system prompt.
```

### Customizing the Success Evaluation Prompt

You can set a custom success evaluation prompt using the `successEvaluationPrompt` property:

```json
{
  "successEvaluationPrompt": "Custom success evaluation prompt text"
}
```

To disable the success evaluation prompt, set it to an empty string `""` or `"off"`:

```json
{
  "successEvaluationPrompt": ""
}
```

## Success Evaluation Rubric

The success evaluation rubric defines the criteria used to evaluate the call's success. The available rubrics are:

* `NumericScale`: A scale of 1 to 10.
* `DescriptiveScale`: A scale of Excellent, Good, Fair, Poor.
* `Checklist`: A checklist of criteria and their status.
* `Matrix`: A grid that evaluates multiple criteria across different performance levels.
* `PercentageScale`: A scale of 0% to 100%.
* `LikertScale`: A scale of Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree.
* `AutomaticRubric`: Automatically break down evaluation into several criteria, each with its own score.
* `PassFail`: A simple 'true' if the call passed, 'false' if not.

### Customizing the Success Evaluation Rubric

You can set a custom success evaluation rubric using the `successEvaluationRubric` property:

```json
{
  "successEvaluationRubric": "NumericScale"
}
```

## Combining Prompts and Rubrics

You can use prompts and rubrics in combination to create detailed instructions for the call analysis:

```json
{
  "successEvaluationPrompt": "Evaluate the call based on these criteria:...",
  "successEvaluationRubric": "Checklist"
}
```

By customizing these properties, you can tailor the call analysis to meet your specific needs and gain valuable insights from your calls.