> ## 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.

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](https://www.adaline.ai/docs/evaluate/llm-as-a-judge.md) with conversation-focused rubrics to assess context retention, coherence, and task completion.
* Track how [cost](https://www.adaline.ai/docs/evaluate/cost.md) and [latency](https://www.adaline.ai/docs/evaluate/latency.md) scale as conversations grow longer.

## Next steps

> [card] [Evaluate Prompts](https://www.adaline.ai/docs/evaluate/evaluate-prompts.md)
>
> Run evaluations on single and chained prompts today.

> [card] [LLM-as-a-Judge](https://www.adaline.ai/docs/evaluate/llm-as-a-judge.md)
>
> Set up qualitative evaluation rubrics.


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