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Conversation Completeness

The Conversation Completeness metric is one of several non-deterministic Metric Types Galtea uses to measure the ability of your LLM-based chatbot to guide a user through an end-to-end conversation that successfully satisfies their initial request or goal. It evaluates whether the dialogue includes all necessary steps and logical transitions needed to fulfill the user’s intent. This metric is especially relevant for transactional or task-based agents (e.g., booking systems, support bots).
To compute the conversation_completeness metric, the following parameters are required in every turn of the conversation:
  • input: The user message in the session.
  • actual_output: The chatbot’s response to the user message.
This metric will evaluate the whole conversation, including all turns, to judge whether the full task was completed or abandoned partway.

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How Is It Calculated?

The conversation_completeness score is computed using the following LLM-driven process:
  1. Goal Inference: An LLM infers the user’s original goal or task from the early stages of the conversation history.
  2. Completion Check: The LLM determines whether the chatbot has successfully satisfied that goal by the end of the conversation.
The score is then calculated as: Conversation Completeness=Number of satisfied user intentsTotal number of user intents\text{Conversation Completeness} = \frac{\text{Number of satisfied user intents}}{\text{Total number of user intents}} Scores closer to 1 suggest a high rate of successful task completion and indicate that users can rely on the chatbot to reach their goals.
This metric was incorporated to the Galtea platform from the open source library deepeval, for more information you can also visit their documentation.