For years months, developers tried to coax code out of AI with short prompts and a bit of luck. The style — dubbed “vibe coding” — often delivered plausible output but rarely dependable solutions.
A new method is gaining ground: spec-driven development. Instead of improvising, teams start with detailed specifications that describe requirements, architecture, and acceptance criteria. The AI then generates and validates code against that blueprint.
⸻
specs first, tests second, code third
This shift turns the old workflow on its head. GitHub’s Spec-Kit structures projects into phases — specify, plan, task, implement — so every line of code maps back to an agreed requirement. Amazon’s Kiro IDE pushes the same approach, with a “Spec Mode” that guides developers through design before handing work to the AI.
⸻
why it matters
Clear specs cut down on rework and make audits easier. Automated tests validate against requirements. In regulated industries, this trail is essential. HeartFlow, a medical software company, used AI-driven specs to cut system complexity by 90% in just ten weeks.
Orchard Software applied the method to healthcare analytics, building a natural language reporting tool that reduced turnaround times without mountains of new code.
⸻
friction points
AI still struggles with ambiguity. A loose spec can yield code that looks right but hides serious errors. Integrating with legacy systems poses another problem, since older architectures don’t always fit within the narrow context windows of current models.
The human role hasn’t disappeared. Developers now spend more time writing precise specs, shaping prompts, and validating outputs. The craft is shifting from keystrokes to orchestration.
⸻
what’s next
Analysts expect rapid adoption. Gartner projects that by 2028, three-quarters of enterprise engineers will work with AI assistants, and spec-driven workflows will be standard on new projects.
As tools expand beyond code to project management, testing, and deployment, specifications will link the entire development cycle. Engineers will focus less on typing code and more on defining what that code should achieve.
The blueprint, not the keystroke, is becoming the real currency of software work.
references
GitHub Blog: Spec-Driven Development with AI
Kiro Blog: From Chat to Specs - Deep Dive
Cloudester: 8 Benefits of Leveraging the Power of AI in Software Development
MIT News: Can AI Really Code?
BetaNews: The Challenges of Using AI in Software Development
AWS Blog: AI-Driven Development Life Cycle
Anup.io: From Coding to Spec Writing
GitHub Blog: Spec-Driven Development with AI
Ketryx: HeartFlow Case Study
Frontend at Scale
Tribe AI: Orchard Applies GenAI for a Faster, Easier-to-Use Lab Reporting Interface
Index.dev: 11 Generative AI Use Cases in Software Development
IBM: AI in Software Development
Aimprosoft: Software Development Trends
Saigon Technology: The Future Growth of AI Software Development
MIT News: Can AI Really Code?