Who is the Spec for: Humans or Models?
The core output of Spec-Driven Development (SDD) is the Spec (technical specification document). However, a fundamental paradox arises in practice: is the primary audience for this Spec a human or an AI model? In theory, the Spec is intended to provide precise requirements and design inputs for AI models, enabling automated or semi-automated code generation. In reality, the accuracy of the Spec must still be reviewed and guaranteed by human engineers, which means they too must read the Spec in detail.
The conflict is that a text-based document, like Markdown, is far less intuitive for expressing complex logic and architecture than visual tools like flowcharts or architectural diagrams. This leads to a poor and inefficient review experience for engineers. One proposed solution is to decouple information consumption for humans and models: while writing a detailed text-based Spec for the model, automatically generate accompanying visual materials like flowcharts specifically for human review.
Spec Management Strategy: Version Piling or Final-State Persistence?
In an agile development environment, an interface or feature can undergo frequent requirement changes. This raises a second question: should a separate Spec version be generated and archived for every single change? Some practitioners favor this approach, hoping to preserve a complete evolutionary history. However, analysis suggests this practice can lead to a rapidly expanding Spec library, creating a deluge of unused intermediate versions and increasing both maintenance costs and information noise.
A more pragmatic strategy might be to abandon excessive process logging and instead focus on maintaining a ‘final-state’ Spec for each feature module or interface. This means that regardless of the number of iterations, the repository retains only one authoritative document describing its current, most accurate state. This approach emphasizes the Spec’s ultimate value as a final repository of project knowledge, rather than a running log of the process.
Development Lifecycle: A Mismatch Between Theory and Reality
The ideal Spec-driven process follows a linear model: Plan -> Decompose -> Implement -> Test -> Archive. However, the real software development process is far from linear. Uncertainties like changing requirements, adjustments to technical solutions, and the discovery and fixing of critical bugs frequently interrupt and reshape the development flow. If one strictly adheres to the theoretical model, a requirement’s Spec might not be ‘archived’ until the last moment before project launch. Throughout this period, this pending Spec becomes a ‘time bomb,’ constantly at risk of being invalidated.
Therefore, it’s crucial to recognize that the iterative cycle of Spec-driven development should not be rigidly tied one-to-one with the lifecycle of a real-world requirement. A more flexible approach is to break down a complex requirement into multiple smaller, more independent development tasks. Each task completes a full micro-cycle of ‘Develop -> Implement -> Archive’ within the SDD framework. When a requirement changes, a new micro-cycle is initiated to address it, rather than continuously modifying a single, massive, and protracted old process. As a result, a single real-world requirement is ultimately fulfilled by the combined outcomes of multiple Spec-driven cycles.
The Fundamental Question: Is the Model + Spec Combination Always Superior?
The Spec-Driven Development paradigm is built on a core belief: Model + Spec + Code > Model + Code. The idea is that a high-quality Spec allows an AI model to understand a project more deeply, thereby generating better output than it would by reading the code alone. But whether this inequality always holds true is the ultimate question all practitioners must face.
The primary challenge is consistency. Similar to the CAP theorem in distributed systems, achieving real-time, perfect consistency between the Spec and the code is an extremely difficult goal. Once the Spec falls behind the actual evolution of the code, this outdated document can become a ‘liability,’ misleading the AI model and degrading its performance. Furthermore, as the context windows of Large Language Models (LLMs) expand towards infinity, models are becoming capable of digesting entire codebases. In this scenario, could well-structured, thoroughly commented, and cleanly named code itself be the best form of ‘Spec’—one that is never out of sync with the implementation?
There is no industry consensus on this yet. A lingering question is whether a ‘scale effect’ exists for high-quality Specs—that is, if reaching a critical mass of Spec quantity and quality can unlock emergent model intelligence and collaborative efficiency far beyond expectations. The answer to this question will determine the ultimate role of Spec-Driven Development in the future of AI-powered software engineering.
