Qlarify's recursively expandable abstracts and retrieval-augmented generation approach have influenced work across several areas: its method of identifying unfamiliar phrases and generating in-context definitions via RAG has been directly adopted for explaining jargon in Terms of Service documents; it is referenced as a representative example of direct-manipulation interactions for steering generative AI, including selecting elements to constrain prompt scope; and it is frequently cited as a motivating example of interactive systems that augment or simplify access to research paper content, interactive summarization interfaces, and LLM-powered tools supporting academic literature review, with its design informing systems in domains ranging from nutritional information accessibility to clinical decision-making.