Multi-Turn Evaluation
Single-turn evaluation misses a critical reality: most AI interactions are conversations. Multi-turn evaluation measures how well a system maintains coherence across turns, uses previous context effectively, degrades gracefully over long conversations, and recovers from errors. This article builds a multi-turn eval harness with per-turn and aggregate scoring.
Quick Reference
- →Conversation coherence: does the model maintain consistent context and persona across turns?
- →Quality degradation: does response quality drop as conversations grow longer?
- →Context utilization: does the model effectively use information from earlier turns?
- →Error recovery: can the model correct course after making a mistake?
- →Evaluate per-turn AND aggregate: per-turn catches local issues, aggregate captures trends
- →Use scripted multi-turn scenarios for reproducible evaluation
Why Single-Turn Evaluation Is Not Enough
Most evaluation benchmarks test a single prompt-response pair. But production AI systems rarely operate in single turns. Customer service conversations average 5-8 turns. Coding assistants maintain context across dozens of exchanges. Research agents iteratively refine answers over multiple rounds. Single-turn evaluation completely misses the challenges of maintaining quality, coherence, and context over extended interactions.
Common multi-turn failures that single-turn evaluation cannot detect: (1) Context amnesia — forgetting information from earlier turns, (2) Persona drift — changing tone, style, or claimed capabilities mid-conversation, (3) Contradiction — directly contradicting something said 3 turns ago, (4) Progressive degradation — each response getting slightly worse, (5) Error amplification — building on a mistake from an earlier turn instead of correcting it.