LLM in research describes the application of large language models to qualitative and consumer insights workflows, where these AI systems process interview transcripts, open-ended responses, and research briefs to surface themes, generate summaries, and produce structured findings. Within AI-moderated research, LLMs serve as the reasoning layer that interprets participant language, adapts interview probes in real time, and synthesizes findings across hundreds of conversations simultaneously. Unlike keyword search or basic text analytics, LLMs understand context, nuance, and sentiment, making them well-suited to the interpretive demands of qualitative methodology. For enterprise insights teams, LLM-powered analysis reduces the manual synthesis burden that has historically made large-scale qual impractical, enabling faster and more consistent thematic analysis across studies.