Multi-Turn Conversation Testing
Multi-turn conversation testing evaluates a model's behavior across an extended back-and-forth exchange rather than a single prompt-response pair — checking whether it maintains context correctly, stays consistent with earlier statements, and doesn't degrade in quality or safety as the conversation gets longer.
Single-turn tests miss an entire category of real failure: a model that answers a first question perfectly can contradict itself by turn eight, forget a constraint the user stated in turn two, or gradually drift out of its intended persona as more context accumulates — none of which shows up if every test case is a fresh, isolated exchange.
Good multi-turn test design uses realistic conversation scripts rather than a list of unrelated questions strung together — a user correcting the model, changing their mind, referring back to something said earlier ("the second option you mentioned"), or gradually escalating a request that should eventually get refused. Each of these targets a distinct failure mode a single-turn test structurally can't catch.
It's also where context window limits become directly relevant to test — a long enough conversation can push earlier instructions out of the effective context, and the failure that causes looks identical to the model simply "forgetting," which is worth distinguishing during debugging.
