Long-context question-answering systems are hard to evaluate: a model may return a fluent answer while missing key evidence, or handle short passages well but lose track of relevant details in longer ones. In a practical guide, Eugene Yan breaks the problem into four parts: evaluation metrics, dataset construction, evaluation methodology, and a review of several long-context QA benchmarks.
Yan argues that metrics should be chosen to match the behaviour being measured, and that evaluation datasets should be designed with the target use case in mind rather than treated as an afterthought. The methodology section, meanwhile, describes how to run evaluations consistently so that results are comparable and trustworthy.
The benchmark review walks through a range of long-context QA datasets and highlights how they differ in what they test. That makes the post a useful starting point for teams building their own evaluations. Since this analysis draws on a single source, there are no competing viewpoints to weigh, but the guide’s practical framing is consistent throughout.