How AI Can Actually Produce a Manufacturable PCB by Splitting Judgment from Geometry

Most 'AI designs your PCB' claims fall short because language models generate plausible-looking layouts that no manufacturer would accept. A more reliable approach splits the design process into two distinct halves: the LLM handles component selection and netlist generation, while a separate deterministic engine handles physical placement and copper routing against hard constraints. This division matters because language models cannot measure clearances or verify trace geometry, making their physical layout estimates untrustworthy. Reproducibility is another key reason to keep routing deterministic — an LLM-routed board can change with every model update or rephrasing, which is unacceptable in hardware development. When routing fails, naming the exact unrouted nets is more useful than a completion percentage, as it directs designers to fix component placement rather than chase a smarter algorithm.