Verifiable Spec-Driven Development with AI

"Coding is largely solved": we have all heard it. What that claim quietly skips is the part where someone has to define what to build. In practice, every serious AI-driven development workflow depends on specifications. The industry's bet is that natural-language specs are good enough to drive autonomous implementation. For a one-shot script or a toy demo, they are.

They stop being good enough the moment a system has more than a handful of features. Prose drifts. A requirement gets added in one document while the API design, the test plan, and the delivery roadmap are never updated to reflect it. The LLM has no way to know this. It faithfully implements a spec that is quietly contradicting itself, and the gap surfaces in production or in a broken integration test three sprints later. The root cause is not the LLM: it is that natural language gives tooling nothing to verify.

This talk argues for a different foundation. It proposes leveraging JSONs to represent specs as code which can be verified. Every aspect of any software system and development cycle can be represented as a JSON spec. Collectively, these specs can define any software system. We can quantify the schema template for each spec and qualitatively define prompt guidance for each aspect. All these specs are sequenced, linked, and worked upon in a strict waterfall model. Since they are defined as JSON artifacts, we can build deterministic tooling to verify, trace, and rectify them. Any non-compliance with the spec sequencing, schema templates, or expected tracing results in self-explanatory errors. This also enables surgical context engineering, so we can precisely work with only what we need. This is the core idea I've been building to address the issues with natural language speccing and AI-driven software development.

Why prose specs drift and why it matters.

How structured JSON turns a spec into a verifiable artifact.

Spec-time contradiction detection catches a whole class of bugs that code review misses.