NLU (natural language understanding) is the AI capability to extract meaning, intent, and relevant information from human language input, going beyond surface text to comprehend what users actually mean.

NLU is the comprehension layer of conversational AI. It transforms raw user input ("yeah I need to change that address from the order I placed yesterday") into structured understanding: intent=address_change, entity=order_date:yesterday, sentiment=neutral. This structured interpretation enables downstream processing—routing, response generation, action execution.

Core NLU tasks include: intent classification (what does the user want), entity extraction (what specific information did they provide), sentiment analysis (how do they feel), coreference resolution (what does "it" refer to), and semantic parsing (understanding relationships in complex sentences). Quality NLU handles the mess of human communication: typos, slang, run-on sentences, implicit meaning.

NLU capabilities have improved dramatically with large language models. Earlier NLU required extensive training data for each intent and entity type. Modern approaches leverage pre-trained models that understand language generally, requiring less domain-specific training. This shifts effort from building NLU to designing what to do with the understanding—workflow execution, response generation, escalation logic.

Related terms: Natural language processing, Intent detection, Intent recognition