Where an eval gives you a pass rate, error analysis explains why the failures happen — grouping them into patterns (a retrieval gap, a prompt ambiguity, a missing tool) so each pattern can be fixed individually. Andrew Ng's August 2026 AI Engineering Skills Map names disciplined evals-plus-error-analysis loops as the core mechanism for making inherently unpredictable AI applications behave reliably, treating it as a distinguishing skill from simply prompting a model and eyeballing the output.