Introduction When working with AMD GPUs and ROCm-based AI workloads, one common issue appears when using uv for Python dependency management. The problem becomes especially visible when setting up projects like ComfyUI on Linux with ROCm-enabled PyTorch builds. Although ROCm-specific wheels are manually installed, running commands such as uv add or dependency synchronization may silently replace ROCm-enabled packages with standard PyPI versions that only support CUDA. This leads to broken GPU acceleration and unexpected runtime failures.

Introduction Spec-driven development is becoming increasingly popular in the era of AI-assisted software engineering. Instead of starting directly from implementation, teams define specifications, domain rules, contracts, and architectural intentions first, allowing Large Language Models (LLMs) and automation tools to generate significant parts of the system. This approach can dramatically improve development speed, documentation quality, and alignment between business and engineering. However, one important challenge emerges quickly: Architecture drift.

Introduction One of the more interesting recent developments in Retrieval-Augmented Generation (RAG) is Knowledge Graph–based RAG. Unlike traditional vector-based retrieval, this approach introduces structured relationships between entities, enabling deeper reasoning and better contextual understanding. This naturally raises a question: Can we build more robust and reliable software systems by combining: Ontologies Domain-specific design models Spec-driven development (SDD) This article explores that idea through a practical comparison.