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.

Building with LLMs: How Spec-Kit Changed My Workflow Over the past three years, I’ve used LLMs—such as ChatGPT, Copilot, and Claude—primarily as assistants to generate code snippets for very specific requirements. They were useful, but always in a limited way: small pieces of logic, isolated helpers, or syntax scaffolding. What I didn’t do until recently was build an entire system around an LLM. The main reason was simple: most of my real work lives inside internal frameworks, legacy systems, and opinionated architectures.