By providing value-based explanations using LIME and adjacent methods, explainable AI can provide insights into AI systems’ black box internal decision-making processes. However, this approach often does not aid laypeople without additional text-based annotations. As such annotations are expensive to produce manually and problematic to provide in real-time, we argue that combining proven decision support systems for the generation of a decision, LIME-like value-based explanations, and corresponding LLM-generated annotations may address these two challenges. More specifically, if LLM generated annotations caused no differences in user perceptions, these could be generated with lower cost and in real-time without downsides. Hence, we conducted an experiment to compare the impact of LLM versus human-generated annotations on user perceptions of a system. Based on metrics commonly assessed for explainable AI systems, our findings suggest annotations produced by LLMs to be comparable to human generated ones, thus suggesting the deployment of such annotations.