> ## Documentation Index
> Fetch the complete documentation index at: https://docs.galtea.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Knowledge Retention

> Checks if your product can follow a multi-turn conversation without losing information.

The **Knowledge Retention** metric is one of several [non-deterministic Metric](/concepts/metric) Galtea uses to evaluate your LLM-based chatbot's ability to retain and consistently apply factual information shared earlier in a conversation. It analyzes the entire conversational history to determine whether the model recalls and reuses relevant facts when generating new responses.

This is particularly useful for long, multi-turn dialogues where context accumulation and memory play a crucial role in the user experience.

## Evaluation Parameters

To compute the `knowledge_retention` metric, the following parameters are required in every turn of the conversation:

* **`input`**: The user message in the conversation.
* **`actual_output`**: The LLM-generated response to the user message.

This metric will evaluate the whole conversation, including all turns, to simulate a memory-check process across multiple turns.

## How Is It Calculated?

The `knowledge_retention` score is computed using an LLM-as-a-judge approach:

1. **Identify Knowledge Anchors**: The LLM scans user inputs to identify specific facts, preferences, constraints, or context (e.g., names, locations, specific numbers).
2. **Verify Recall**: The LLM checks if the agent recalled and applied this information in subsequent turns.
3. **Check Consistency**: The LLM evaluates whether the agent contradicted previously established information, asked for information already provided, or ignored constraints set earlier.

The metric assigns a **binary score**:

* **Score 1.0 (Good Retention):** The agent correctly recalled relevant information or no specific memory recall was required (and no errors were made).
* **Score 0.0 (Poor Retention):** The agent forgot information, contradicted itself, or asked redundant questions about known facts.

## Suggested Test Case Types

The Knowledge Retention metric is effective for evaluating Behavior test cases in Galtea, particularly:

* **Long multi-turn conversations** where the user shares preferences, constraints, or facts early on.
* **Personalized assistant scenarios** where the agent must recall user-provided details.
* **Complex workflows** where information from one step is needed in a later step.