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

# Evaluate multi-turn chat

> Run evaluations on conversational prompts where context builds across multiple exchanges

<Note>
  **Coming soon** — Multi-turn chat evaluation is currently under development. This page will be updated when the feature is available.
</Note>

Multi-turn chat evaluation will let you assess conversational AI systems where the quality of responses depends on context accumulated across multiple user-assistant exchanges. This is essential for chatbots, customer support agents, and any prompt that maintains state across turns.

## What to expect

* Evaluate how well models **maintain context** across turns, handle **references** to previous messages, and **stay consistent** with persona and instructions throughout a conversation.
* Structure datasets with full conversation histories so each test case represents a complete conversation state.
* Use evaluators like [LLM-as-a-Judge](/docs/evaluate/llm-as-a-judge) with conversation-focused rubrics to assess context retention, coherence, and task completion.
* Track how [cost](/docs/evaluate/cost) and [latency](/docs/evaluate/latency) scale as conversations grow longer.

## Next steps

<CardGroup cols={2}>
  <Card title="Evaluate Prompts" icon="play" href="/docs/evaluate/evaluate-prompts">
    Run evaluations on single and chained prompts today.
  </Card>

  <Card title="LLM-as-a-Judge" icon="gavel" href="/docs/evaluate/llm-as-a-judge">
    Set up qualitative evaluation rubrics.
  </Card>
</CardGroup>