The slogan "AI, write code for me" has sparked an experiential revolution in the developer community over the past two years.
Suddenly, generating a runnable module with natural language wasn't a fantasy. You could write a single prompt and get a UI, an API, or even test cases. This gave rise to "vibe coding": turning development into a real-time dialogue with a model, radically compressing the distance between an idea and its implementation.
But when we shift our focus from "solo prototypes" back to "engineering projects," teamwork, and long-term maintainability, the fundamental flaws of vibe coding are laid bare.
At the same time, a more systematic, engineering-focused paradigm—Specification-Driven Development (SDD)—is emerging. With its superior control, verifiability, and scalability, SDD is becoming the essential path to bringing AI programming into production-grade applications. AWS's Kiro is a prime example of this practice, explicitly positioning "specifications" as the bridge from prototype to production.
Here, I'll break down—through problems, comparisons, evidence, and practical advice—why engineering-grade AI programming requires SDD, and I'll articulate Crevo's perspective on this practice.
I. The Charm and Core Flaws of "Vibe Coding"
The Charm (Why it's so popular)
- Extremely Low Barrier: Natural language produces runnable results. This provides a rapid feedback loop, perfect for quick proofs-of-concept (POC).
- High-Speed Prototyping: In the early stages, "vibing" can build a demo-ready prototype incredibly fast, saving significant manpower.
The Core Flaws (Why it's not for engineering)
- Semantic Drift and Uncertainty: Models make heavy assumptions when interpreting vague needs. The output often misaligns with the team's intent, and it's difficult to ensure boundary conditions and error handling are covered.
- Poor Interpretability/Auditability: A "vibe" output is often a "black box" code snippet. It lacks source attribution, design decisions, or acceptance criteria, making it nearly impossible for teams to review or audit.
- Knowledge Ossification Risk: When code and design aren't synced into a machine-readable spec, that knowledge remains in human brains or gets scattered across PR comments, easily lost with team turnover.
- Technical Debt and Maintenance Costs: It’s fast in the short term but creates massive, hidden technical debt in the long term, especially in large systems relying on auto-generated code.
- Collaboration Conflict: Code generated by different team members, each "vibing" with a model, leads to fragmentation in style, interfaces, and test coverage if there's no unifying standard.
These aren't minor implementation details; they are a core threat to engineering sustainability. Real-world production systems cannot be maintained by "getting lucky with a prompt" again and again.
II. What is Spec-Driven Development (SDD)? The Core Value
The basic logic of SDD is simple but profound: Make the "specification" the central single source of truth for both collaboration and automation.
In an SDD workflow, natural language or product requirements don't drive code directly. Instead, they are*first*translated into structured, verifiable, and executable specifications (including requirements, acceptance criteria, API contracts, test scenarios, design points, etc.).Then, automated agents and/or humans produce code, tests, and deployments based on that spec.
Key Benefits of SDD:
- Consistency and Verifiability: The implementation can be automatically checked against the spec. Tests are based directly on the spec's acceptance criteria, dramatically reducing errors.
- Auditability and Traceability: Every implementation point has a spec reference. Changes are recorded, making it easy to trace decisions and ensure compliance.
- Reusability and Modularity: Well-structured specs can be reused across multiple projects, improving engineering efficiency.
- Team-Friendly Collaboration: The spec acts as a communication contract. Design, Product, Engineering, and QA can all share the same source of truth, reducing communication overhead.
These aren't abstract ideals; they are direct, practical requirements for maintainability, scalability, and compliance. As initial reviews of SDD on dev.to also note: SDD introduces an upfront specification cost, but it's the key factor that elevates AI-assisted development from a "prototyping tool" to a "production tool" in the long run.
III. Why Amazon is Betting on SDD as the Solution
AWS (and tools in its ecosystem, like Kiro) is using SDD as the practical answer to the chaos of vibe-driven development. Kiro's approach is worth watching because it turns this philosophy into engineered product features:
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From Prompt to Spec:It translates natural language prompts into structured documents like
requirements.md,design.md, andtasksto serve as a baseline for generation. - Agents and Hooks: It uses agents to automatically execute tasks, run tests, and update the spec, keeping the implementation and the specification in sync.
- Review and Guardrails: It allows users to approve critical changes before automation proceeds, preventing models from blindly altering production code.
- Integrates with Existing Codebases: Kiro can read project context and infer constraints from existing code, improving the quality of its generated output.
Multiple media outlets and hands-on reviews have pointed out that Kiro is gaining traction because it solves the disconnect between vibe coding and engineering—it connects "rapid generation" with "engineering quality." Hands-on reports also state clearly: without specs, AI generation is difficult to scale to production; Kiro solves this through spec-driven execution.
In short: Kiro isn't against generation; it's here to give generation discipline.
IV. Addressing Common Objections: Is the "Upfront Cost" of SDD Unacceptable?
Objection 1: "SDD requires writing specs first, which will slow down development."
Response: This is a "short-term investment for long-term returns" choice. Proper SDD isn't about writing massive, dead documents. It's about using tools toautomate spec generation, turning that upfront labor into a one-time modeling cost that is then maintained by machines. Many organizations would rather invest a bit more upfront than pay the exorbitant maintenance costs in production. Furthermore, toolchains from GitHub and AWS are already moving to automate spec writing, lowering this barrier to entry.
Objection 2: "What if the model misunderstands the spec?"
Response: This is precisely the point of SDD. It's not about blindly trusting the model's understanding. It's about having the model output its understanding as verifiable units (clear acceptance criteria, examples, boundary tests), and then using automated validation to constrain the final result. This creates a closed loop: Intent → Spec → Generation → Validation → Correction. This is far more controllable than relying on prompt-trial-and-error.
V. Practical Advice: How to Introduce SDD into Your Workflow
Here are actionable steps for teams aiming to combine the "speed of vibe coding" with the "engineering quality of SDD."
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Keep the "Vibe" Entry Point, but "Immediately Structure" the Output:
Allow developers to use free-form natural language to generate prototypes. But the tool (or platform) must automatically convert these conversations into structured spec drafts (with acceptance criteria and examples). This way, the prototype doesn't remain a one-off—it immediately enters a governed, spec-based loop. Kiro's model is a perfect example.
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Enforce "Verifiable Spec Units":
Every feature must be accompanied by at least one executable acceptance test or clear boundary cases. The test is both part of the spec and an automatic verifier for the model's output.
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Adopt Lightweight Spec Templates and Iterative Processes:
Don't make specs heavy, burdensome documents. Use modular templates (requirements, API contracts, example request/response, key use cases) and bring them into version control and your code review process.
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Establish a Two-Way Sync: Implementation → Spec:
A change in the implementation should trigger a process to write back to the spec or at least flag a discrepancy (e.g., via CI checks or agent hooks). This ensures the spec is a "living document," not a "write-and-forget" one. Kiro's hook model is a practical application of this.
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Empower the Team with "Review Mechanisms," Not "Blind Trust":
Retain human approval nodes in the production path (especially for critical modules, permissions, data access, etc.) to ensure safety and compliance. Let AI do the implementation, but let the team make the final decisions.
VI. Crevo's Design Stance
Crevo's goal is to combine the "convenience of natural language"with the"control of engineering specifications." Our design philosophy has three pillars:
- Language as the Entry Point, Specs as the Core: Users can start their design and development process with natural language. But Crevo translates these conversations into structured, verifiable system descriptions (PRD-style smart drafts + API contracts + acceptance criteria), turning "casual prompts" into reviewable engineering facts.
- Specs as Executable First-Class Citizens: Specs aren't just documents; they are executable objects that trigger automated generation, testing, and deployment. Every change must have a verifiable unit for spec-driven generation to become a sustainable engineering practice.
- A Path for Progressive Adoption:We don't demand teams abandon their existing workflows overnight. Crevo supports a gradual process of "vibe → auto-spec → iterate," allowing teams to adopt SDD-style practices at their own pace, building trust as they go.
To put it more bluntly: Crevo is designed to package the "speed of vibe" with the "reliability of SDD."
VII. Conclusion: Why We Must Pay Attention to SDD Now
Vibe coding was a revolutionary experience, but it is not the engineering end-state. If we want to bring AI into large-scale production systems, we must embed specifications, validation, and synchronization into the generation process.
SDD provides AI programming with engineering semantics, a validation loop, and a team contract. It's the essential step to move from "tools" to "platforms" and "processes." Products like Kiro are solving the engineering problems created by "vibe" by making specs the core of their system.
My advice to developers and teams: enjoy the speed of vibe, but don't abandon the spec. Turn the specification into a "living asset" automatically maintained by tools, not a heavy paper document.
Only then can AI truly evolve from an "assistant" to an "engineering partner."
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Original Link
References / Further Reading
- Daniel Sogl, "Spec Driven Development (SDD) — A initial review." on dev.to.
- Kiro project / docs / blog — “The AI IDE for prototype to production”.
- Hands-on articles and reviews: Reports from Dev class, The New Stack, etc., discussing the value and challenges of SDD in engineering.
- Articles from GitHub / Microsoft / AWS on Spec-Driven toolchains and open-source kits, illustrating the industry's move to support SDD.
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