API to evaluate a conversation transcript based on user defined attributes (e.g. clarity of responses, courtesy, listening skills, and more).
This API is accessible only with the Scale and Enterprise plans.
Convai's Evaluation API endpoint
POST https://api.convai.com/character/evaluate_conversation
This API allows you to evaluate a conversation transcript based on user defined attributes e.g. clarity of responses, courtesy, listening skills, and more. The evaluation will be returned in a structured JSON format.
Headers
Name
Type
Description
CONVAI-API-KEY*
String
The unique api-key provided for every account.
Request Body
Name
Type
Description
session_id
String (required)
The ID of the session you want to evaluate.
character_id
String (required)
The ID of the character performing the evaluation.
prompt
String (required)
A predefined or custom prompt containing the transcript and specific instructions for evaluation.
variables
JSON Object (optional)
A set of key-value pairs providing additional data required for evaluation (e.g., customer name, item details). This depends on the prompt being passed.
You are an human analyst designated to evaluate employee-customer interactions in a given scenario. Based on the conversation transcript provided below, analyze the interaction and provide an evaluation according to the specified attributes. Each attribute should be rated on a scale of Excellent, Good, Fair, or Needs Improvement. Provide specific examples from the transcript to support your rating. Return the evaluation results in a structured JSON format for easy parsing.
Transcript:
[[conversation_history]]
Here are some data that might be needed to evaluate the conversations:
Actual Order: [[item_details]]
Customer Name: [[customer_name]]
Customer Age: [[customer_age]]
Please evaluate the above conversation based on the attributes listed and provide a Rating and Feedback for each of them. If an attribute is not applicable or cannot be assessed, return null in the json output. Return the result in the following format:
{
"evaluation": {
"correctness_of_responses": {
"rating": "",
"feedback": "[Did the employee place the actual order correctly based on the customer's request?]"
},
"clarity_of_responses": {
"rating": "]",
"feedback": "[Did the employee's responses appear clear and easy to understand based on the text provided?]"
},
"conciseness_and_relevance": {
"rating": "",
"feedback": "[Were the employee’s responses concise and focused on the relevant details, without unnecessary information?]"
},
"courtesy_and_respect": {
"rating": "",
"feedback": "[Did the employee demonstrate politeness and respect toward the customer in their language and responses?]"
},
"listening_skills": {
"rating": "",
"feedback": "[Did the employee respond appropriately, indicating they were actively listening and addressing customer concerns?]"
},
"product_knowledge": {
"rating": "",
"feedback": "[Did the employee demonstrate accurate knowledge of the menu, promotions, or policies?]"
},
"problem_solving_and_issue_resolution": {
"rating": "",
"feedback": "[Evaluate how well the employee addressed and resolved issues]"
},
"response_time": {
"rating": "",
"feedback": "[Analyze the flow of the conversation and whether there were any delays in response]"
},
"order_accuracy": {
"rating": "",
"feedback": "[Evaluate the accuracy of order confirmation, if applicable]"
},
"follow_up_and_conversation_closure": {
"rating": "",
"feedback": "[Review how the conversation was closed and whether appropriate follow-up occurred]"
},
"overall_summary": {
"overall_rating": "",
"overall_feedback": "[Provide a brief summary of the employee's performance across all attributes]"
}
}
}
If you focus on the prompt, there are certain text within [[ ]] . These are expected-variables. Now, [[conversation_history]] is a compulsory expected-variable, that has to be present in the prompt. The rest of them depends on your requirements, to be passed to the prompt as needed.
So the variables key, in the body of the request, should be of length expected-variables - 1, i.e, there should be values for all the other keys mentioned in the [[ ]] brackets, except for conversation_history which is fetched from the session_id provided. The variables list can be empty if you are passing no other expected-variables in the prompt.
Not passing the conversation_history variable in the prompt will lead to 500 error response from the server.
Response
On success, the API returns a structured evaluation of the conversation, covering multiple attributes.
{
"evaluation": {
"clarity_of_responses": {
"rating": "Excellent",
"feedback": "The employee provided clear and concise responses throughout the conversation."
},
"conciseness_and_relevance": {
"rating": "Good",
"feedback": "Responses were relevant but could be more concise."
},
"courtesy_and_respect": {
"rating": "Excellent",
"feedback": "The employee was polite and respectful."
},
"listening_skills": {
"rating": "Good",
"feedback": "The employee responded appropriately, though some concerns were addressed after a delay."
},
"product_knowledge": {
"rating": "Excellent",
"feedback": "The employee demonstrated accurate knowledge of the menu."
},
"problem_solving_and_issue_resolution": {
"rating": "Needs Improvement",
"feedback": "Issue resolution took longer than expected."
},
"response_time": {
"rating": "Fair",
"feedback": "There were noticeable delays between responses."
},
"order_accuracy": {
"rating": "Excellent",
"feedback": "All items were confirmed accurately."
},
"follow_up_and_conversation_closure": {
"rating": "Good",
"feedback": "The employee followed up appropriately, though the closure could have been smoother."
},
"overall_summary": {
"overall_rating": "Good",
"overall_feedback": "The employee's performance was generally good, but there is room for improvement in responsiveness."
}
}
}
{
"ERROR": "Invalid API key provided."
}
{
"ERROR": "Invalid variable list. Please recheck the variables being sent",
"Reference ID": "<REFERENCE-ID>"
}
