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Knowledge Retention

Knowledge Retention is a multi-turn metric to determine if your chatbot remembers details well.

Overview

The knowledge retention metric is a multi-turn metric that uses LLM-as-a-judge to evaluate whether your chatbot remembers important information provided by the user throughout the conversation.

Required Parameters

These are the parameters you must supply in your test case to run evaluations for knowledge retention 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 knowledge retention metric first uses an LLM to extract information from the content of all user turns, then uses the same LLM to check if any assistant turns contain content that fails to recall this information.


Knowledge Retention=Number of Assistant Turns without Knowledge AttritionsTotal Number of Assistant Turns\text{Knowledge Retention} = \frac{\text{Number of Assistant Turns without Knowledge Attritions}}{\text{Total Number of Assistant Turns}}

The final score is the proportion of assisant turns with knowledge attrition found in the conversation.

Usage

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

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

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