This study evaluates large language models’ (LLMs) reasoning capabilities in spoken language understanding (SLU) during interpersonal interactions. We incorporated several factors into LLM prompts: instructing via examples (IE), integrating domain knowledge (DK), and including context (IC). Experiments with Gemini-1.5-pro, GPT-3.5-turbo, and GPT-4o were conducted on an SLU task that classifies the degree of explanation (i.e., under-explained, succinct, comprehensive, over-explained) in job interview responses—an important step toward developing automatic interview training systems. Results demonstrate the feasibility of few-shot (1- to 4-shot) learning, with ablation studies confirming that modifications to prompts, especially when combining IE, DK, and IC, lead to further performance improvements.