The growing application of Large Language Models (LLMs) in text simplification holds significant promise for improving accessibility for non-native speakers and individuals with learning or cognitive disabilities. We examine the effectiveness of LLMs in generating Easy German responses through a domain-agnostic Question-Answering (QA) framework, leveraging explicit simplification rules and tailored prompting strategies. Focused on health-related questions, the framework transforms complex medical information into accessible language to enhance health literacy. Automated readability and semantic alignment metrics are combined with human evaluations from non-native speakers (A1-B1 CEFR proficiency) and individuals with special needs. Results show that GPT-4 consistently outperforms open-source models like Llama and Mixtral, generating factually accurate, clear, and accessible responses, while the latter models often struggle with coherence. Though developed for Easy German, the domain-agnostic methodology can be adapted to any language with minimal prompt adjustments. For reproducibility and material, please refer to our GitHub repository \(^{1}\) https://github.com/raeesay/easy-german-qa .