# Chat Metrics

### Chat Metrics

**Chat Metrics** within the RagaAI Metric Library provide targeted insights into conversational performance—measuring how effectively your agents comply with instructions, engage users, and respond accurately in context.

Supported metrics include:

{% content-ref url="/pages/AGMzo5LcsR4zAmqRKGCC" %}
[Agent Quality Chat Metric](/ragaai-catalyst/ragaai-metric-library/chat-metrics/agent-quality.md)
{% endcontent-ref %}

{% content-ref url="/pages/NPHVJUZWB2TZr3oROWT8" %}
[Instruction Adherence Chat Metric](/ragaai-catalyst/ragaai-metric-library/chat-metrics/instruction-adherence.md)
{% endcontent-ref %}

{% content-ref url="/pages/H0Oajr7dEDdN1xkOMQww" %}
[User Chat Quality](/ragaai-catalyst/ragaai-metric-library/chat-metrics/user-chat-quality.md)
{% endcontent-ref %}

### Why Chat Metrics Matter

* Assess **response relevance and coherence** : crucial for user satisfaction in chat agents.
* Ensure **adherence to prompts and system instructions** : especially in regulated or brand-aligned workflows.
* Track **user-side clarity** to diagnose miscommunication risks.
* Support **iterative improvement** by emitting quantifiable scores for agent tuning and prompt refinement.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/chat-metrics.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.