What is preventing us from building a NLP system that could help real people in real situations, for instance when they need legal advice but don’t understand law? This question is trickier than one might think, because legal systems vary from country to country, so do the law books, availability of data, and incomprehensibility of legalese. In this paper we focus Germany (which employs the civil-law system where, roughly speaking, interpretation of law codes dominates over precedence) and lay a foundational work to address the laymen’s legal question answering empirically. We create GerLayQA, a new dataset comprising of 21k laymen’s legal questions paired with answers from lawyers and grounded to concrete law book paragraphs. We experiment with a variety of retrieval and answer generation models and provide an in-depth analysis of limitations, which helps us to provide first empirical answers to the question above.