How to Evaluate Multi-Turn LLM Conversations
Explore how to evaluate complete multi-turn conversations in LLM systems by focusing on user goals, identifying the earliest failure point, and simplifying failures to minimal reproducible cases. Understand tracing techniques and how to test handoffs effectively, ensuring your AI systems maintain coherent, goal-directed dialogue flow.
Once a system handles real users end-to-end, failures stop appearing as isolated wrong answers and instead surface as broken interactions. A single flawed follow-up question can divert an entire conversation into a dead end. A tool call that returns incomplete data can echo several turns later as an unrelated hallucination. At this stage, the unit of evaluation shifts from individual responses to the entire session. The focus shifts from checking whether a specific turn was correct to evaluating whether the system, as a whole, guided the user toward their actual goal.
This lesson focuses on making that shift. You already know how to evaluate single turns and identify discrete failure modes. Now you need a workflow that allows you to evaluate multi-turn traces quickly, consistently, and in a manner that directly feeds into your development loop. The goal is not to label everything. The goal is to understand the path the system took, pinpoint the moment where it veered off course, and convert that moment into a fixable mechanism inside your product.
Most multi-turn failures collapse to something simple. The system missed a clarification, dropped a key context detail, or trusted a misaligned tool result. These issues only become obvious when you review traces with a sequence-level lens, following how each turn shaped the next rather than judging them in isolation.
How do I evaluate the entire conversation and capture the right traces?
When reviewing a long trace, avoid starting with tool calls or internal reasoning. Start with the transcript exactly as it appeared to the user. Read the conversation from top to bottom and ask a single question: whether the system achieved the user’s goal. This framing helps prevent premature focus on granular technical details before the high-level outcome is clear.
How do you identify the root cause in a multi-turn failure?
Once you have an overall judgment, pass or fail, locate the first turn where the trajectory shifted. Treat ...