- In short
- Multi-turn eval design means building a separate golden dataset of full conversation transcripts, each with known high-quality responses at every turn, to evaluate a conversational system across an extended conversation. It scores whether prior context is kept straight, whether follow-ups are answered without inventing earlier details, and whether quality holds as the conversation lengthens. A single-turn eval that scores individual prompt-response pairs cannot certify these properties, and the transcript dataset must cover the follow-ups, topic shifts, and conversation lengths the system will actually see in production.
Full concept guide coming soon
We are building the in-depth, exam-aligned guide for this knowledge point. In the meantime, explore the prerequisites and related concepts below, watch the official Anthropic Academy lessons, and start an adaptive study session to master it with Archie.
Watch and learn
Official Anthropic Academy lessons first, then hand-picked walkthroughs. Videos load only when you press play.
No videos curated for this concept yet
We are still curating the best official and community videos for this topic.
References & primary sources
Adaptive study
Master this concept with Archie
Practice it inside an adaptive study session. Archie, your Socratic AI tutor, tracks your mastery with Bayesian Knowledge Tracing and schedules the perfect next review.