NLU Evaluation — Model Quality Assessment & Improvement Plan

I will conduct a comprehensive evaluation and error analysis of your existing NLU model — measuring intent classification accuracy, entity F1 by type, confusion matrix for intent disambiguation, out-of-scope detection precision and recall, and analysing the failure patterns to identify training data gaps, intent boundary problems, and entity extraction inconsistencies driving production errors. NLU model evaluation that reports only overall accuracy gives a misleading picture of model quality — an 85% accurate NLU model that fails consistently on your most business-critical intents is more problematic than a 78% accurate model whose failures are spread evenly across less important intents.

The evaluation covers intent precision/recall/F1 by intent, entity F1 by entity type, confusion matrix heatmap, out-of-scope detection evaluation, error pattern analysis categorising failure modes, and a prioritised improvement plan targeting training data additions and intent boundary clarifications for the highest-impact failure categories.