AI-enabled assistants are now integrated into Flare™ to augment both the graphical application and the Flare Python API (PyFlare). The objective is to improve task efficiency and reduce barriers for users with varying levels of experience in programming and computational chemistry. In this session, we will demonstrate how the Chat Model (doc-aware guidance for GUI features) and the Code Model (runnable PyFlare™ snippets and explanations) of the AI assistants can streamline everyday workflows while preserving scientific rigor. We will cover recommended practices and prompting strategies for interacting with the AI assistants in order to maximize productivity for users with varied levels of expertise in computational chemistry and coding.