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Conversation Completeness

Conversation Completeness is a multi-turn metric to determine if a conversation is complete.

Overview

The conversational completeness metric is a multi-turn metric that uses LLM-as-a-judge to evaluate whether your chatbot satisfies the user’s requirements at each turn throughout the conversation.

Required Parameters

These are the parameters you must supply in your test case to run evaluations for conversation completeness metric:

turnslist of TurnRequired

A list of Turns as exchanges between user and assistant.

Parameters of Turn:

roleuser | assistantRequired

The role of the person speaking, it's either user or assistant

contentstringRequired

The content provided by the role for the turn

How Is It Calculated?

The conversation completeness metric first extracts distinct user intentions from all turns using an LLM, then uses the same LLM to check if the corresponding assistant turns have satisfied those intentions.


Conversation Completeness=Number of Satisfied User Intentions in ConversationTotal Number of User Intentions in Conversation\text{Conversation Completeness} = \frac{\text{Number of Satisfied User Intentions in Conversation}}{\text{Total Number of User Intentions in Conversation}}

The final score is the proportion of satisfied user intentions found in the conversation.

Usage

To run the conversation completeness metric on Confident AI, add it to a single-turn metric collection. This will allow you to use conversation completeness metric for:

  • Multi-turn E2E testing
  • Online and offline evals for traces and spans

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