> For the complete documentation index, see [llms.txt](https://docs.raga.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/chat-metrics.md).

# Chat Metrics

Chat Metrics in the RagaAI Metric Library evaluate conversational AI quality. Measuring instruction adherence, user clarity, agent helpfulness, coherence, and overall dialogue performance.

### 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.