In this paper we present ViterbiPlanNet to address the limitations of implicit learning in procedural planning by explicitly grounding knowledge in external structures. Rather than relying on massive models to blindly memorize task rules we introduce a differentiable layer that embeds a procedural knowledge graph directly into the end to end training process. This approach shifts the learning burden from brute force parameter scaling to structure aware optimization acting as an algorithmic guardrail. Ultimately we demonstrate that grounding neural networks in these external constraints drastically reduces computational costs improves sample efficiency and produces highly robust sequence predictions.