Voting Advice Applications (VAAs) have been crucial in modern elections, helping voters understand political issues and party positions. Recent innovations, such as Conversational Agent Voting Advice Applications (CAVAAs), enhance the user experience by integrating chatbots that address comprehension issues, helping to provide more accurate voting advice. However, current rule-based CAVAA chatbots face limitations due to the need for predefined responses and restricted question coverage. This paper introduces two new Generative AI chatbot designs -open and semi-open generative chatbots- using Retrieval-Augmented Generation (RAG) and open-source Large Language Models (LLMs). These models generate accurate, context-specific responses to political questions, addressing the limitations of rule-based systems while incorporating filters to avoid biased or inappropriate answers. Tested, in a German case study, using the German 2021 Wahl-O-Mat, the chatbots were evaluated through a two-stage process: coder grading of responses and an expert survey of VAA Creators (N = 13). Results demonstrate that the generative chatbots effectively answered both standard CAVAA questions and broader political inquiries, with high acceptance and perceived usefulness among experts. Despite these successes, further refinement is needed to improve filtering and ensure unbiased interactions. These findings offer valuable insights into the use of Generative AI in (CA)VAAs and its potential to improve voter guidance.