Program Synthesis (PS) is the task of automatically generating logical procedures or source code from a small set of input-output examples. While LLMs and agents dominate current AI conversations, they often struggle with these kinds of precise reasoning tasks—where smaller, well-structured models for PS can succeed. In this talk, we’ll walk through the end-to-end development of an PS system, covering dataset representation using graph structures, model architectures, and tree search algorithms. The working example for this talk is the generation of procedural textures for 3D modeling, but the methodology is domain-agnostic. Participants will leave with a deeper understanding of PS, its real-world potential, and the trade-offs between different architectural approaches. The session is designed for practitioners with a solid understanding of ML concepts and some familiarity with NN architectures such as transformers and CNNs.