Large Language Models (LLMs) offer transformative potential for mathematical toolpath generation in additive manufacturing, yet their use introduces risks such as hallucination and impractical geometry. This talk presents a novel workflow developed specifically to eliminate these risks, ensuring a 100% success rate in generating mathematically-defined geometry and toolpaths. Central to this approach is the integration of expert process knowledge, manufacturing system constraints, and tailored use of LLM capabilities, enabling non-mathematicians to effectively design and implement advanced geometries previously inaccessible even with human mathematical expertise. The methodology significantly reduces cognitive demands on LLMs, enabling the use of lightweight models that run directly on local hardware or edge devices. This is particularly important for fields where data-security requirements limit the use of cloud services or for deployed applications with limited internet connectivity. To validate its end-to-end practicality, the approach is integrated with FullControl software for toolpath design, ensuring the generated designs are immediately manufacturable with guaranteed reliability. This approach fundamentally expands the capabilities of additive manufacturing design and control, unlocking opportunities to surpass the current state-of-the-art in diverse ways - for example, enabling single-object definition of multi-axis robotic additive manufacturing toolpaths at complexity levels unattainable through existing workflows that rely on multiple model translations. The tools and techniques discussed are currently available in beta form and will be fully released as open-source software this summer, enabling immediate community use and contribution.