Spec-Driven Development — Bridging PMs and AI

We are building a custom AI agent that takes detailed specifications and generates initial code implementations. The early results taught me something unexpected: the quality of AI-generated code is directly proportional to the quality of the specification.

The Old Way

In traditional development, a PM writes a user story, the developer interprets it, asks clarifying questions, and eventually builds something. The spec can be ambiguous because the human developer fills in gaps using context and judgment.

The New Way

When an AI agent generates code from a spec, ambiguity becomes bugs. If the spec says "handle errors appropriately," the AI will generate generic error handling that may or may not match your requirements. If the spec says "return a 429 status code with a Retry-After header when rate limited, log the event to our monitoring service, and queue the request for retry after the specified interval," the AI generates exactly that.

What Good Specs Look Like Now

I restructured our specification template to be AI-friendly. Every spec now includes explicit input and output contracts with data types, error scenarios with expected behavior for each, performance requirements with specific thresholds, and integration points with exact API contracts.

This is more work upfront. A spec that used to take me an hour now takes two. But the downstream time savings are significant because the AI-generated first draft is closer to production quality, and the developer spends less time on interpretation.

The PM Implication

PMs who write precise specifications are suddenly more valuable than ever. The skill of translating business requirements into unambiguous technical specifications is exactly what AI-assisted development demands. If you have been told that detailed specs are waterfall thinking, reconsider. In the AI era, specification quality is the bottleneck.