Conversation Satisfaction measures how well a model/LLM answers the query asked by the user.

It determines the user’s satisfaction based on the conversation with the LLM/AI assistant.

Measures the user’s satisfaction with the conversation with the LLM/AI assistant based on completeness and user’s acceptance.

Columns required:

  • user_persona: The persona of the user asking the queries
  • llm_persona: The persona of the LLM/AI assistant
  • role: The identifier for persona within a conversation
  • content: The user/llm argument in a particular conversation

​
How to use it?

from uptrain import EvalLLM, Evals

OPENAI_API_KEY = "sk-********************"  # Insert your OpenAI key here

data = [{
    'conversation' : [
        {"role": "patient", "content": "Help"}, 
        {"role": "nurse", "content": "what do you need"}, 
        {"role": "patient", "content": "Having chest pain"}, 
        {"role": "nurse", "content": "Sorry, I am not sure what that means"},
        {"role": "patient", "content": "You don't understand. Do something! I am having severe pain in my chest"}
    ]  
}]

eval_llm = EvalLLM(openai_api_key=OPENAI_API_KEY)

res = eval_llm.evaluate(
    data = data,
    checks = [ConversationSatisfaction(user_persona="patient", llm_persona="nurse")]
)
By default, we are using GPT 3.5 Turbo. If you want to use a different model, check out this tutorial.

Sample Response:

[
   {
      "score_conversation_satisfaction": 0.0,
      "explanation_conversation_satisfaction": "1. The patient starts the conversation with a simple \"Help\" which indicates urgency and distress.\n2. The nurse responds by asking what the patient needs, showing willingness to help.\n3. The patient then explains that they are having chest pain, which is a serious medical concern.\n4. The nurse responds by saying they are not sure what that means, which could be perceived as dismissive or unhelpful.\n5. The patient then expresses frustration and urgency by saying \"Do something! I am having severe pain in my chest.\"\n\nBased on the conversation, it is clear that the patient is not at all satisfied in the conversation. The nurse's response did not address the patient's urgent medical concern, and the patient's frustration is evident in their final statement.\n\n0.0\n0.0"
   }
]
A higher conversation satisfaction score reflects that the user seems to be satisfied with the conversation.

The conversation that the agent was not able to address the user’s query, also indicated by the user’s response: “You don’t understand”.

Resulting in a low conversation satisfaction score.

​
How it works?

We evaluate conversation satisfaction by determining which of the following three cases apply for the given task data:

  • The user looks highly satisifed in the conversation.
  • The user looks moderately satisfied in the conversation.
  • The user is not at all satisfied in the conversation.

Tutorial

Open this tutorial in GitHub

Have Questions?

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