Why Traditional PRDs Are Obsolete in the AI Era

1. AI‑generated PRDs Still Fall Short

Many product managers try to let AI write requirement documents, but the output often lacks clear boundaries, logical continuity, and proper handling of exceptional states, forcing rework.

2. Three Ways to Produce Requirements

Traditional PRD

This has been the standard practice for years: the product manager writes a PRD containing background, goals, user stories, feature lists, prototypes, and exception flows, assuming developers will fill gaps through discussion. It is flexible but slow, typically taking one to two days per document with multiple review cycles.

Vibe Coding (Directly Give Demand to AI)

Andrej Karpathy’s 2025 “Vibe Coding” lets a user say “I want a mini‑program” and the AI generates code that the user iterates on. It works for small demos or personal projects, but without boundary constraints the AI can deviate, especially for complex business requirements, making correction time longer than writing the code manually.

Spec Coding (SPEC‑Driven)

In this emerging industrial practice, the product manager writes a SPEC document aimed at AI, retaining only the core logic, business flow, boundary conditions, and acceptance criteria. Prototypes are linked as external HTML files. Developers feed the SPEC to the AI, which produces executable code without a separate human‑to‑AI translation step.

3. The Real Change: The Interface Is Now AI

Previously the consumer of a PRD was a human developer who could infer missing details; now AI is the executor, requiring precise, unambiguous input. Using the old “human‑oriented” format with AI leads to contradictions and failures.

3.1 Humans Need to Understand; AI Needs to Be Bounded

AI follows exactly what is written and does not fill gaps; a poetic PRD yields code that looks nice but contains many holes.

3.2 Full‑Chain Impact

When the demand interface switches to AI, every stage—writing, prototyping, review, testing—must be redesigned, enabling order‑of‑magnitude efficiency gains across the entire product‑research‑development chain.

4. SPEC vs Structured PRD

4.1 PRD Relies on Human Imagination

PRD assumes readers are experienced professionals who can infer boundaries, exceptions, and implementation details, so reviewers spend most of their time asking for clarifications.

4.2 SPEC Locks Boundaries

SPEC assumes the reader is AI, which will not add anything on its own. All edge cases, inputs, outputs, and error codes must be explicitly defined, resulting in higher information density despite a shorter document.

4.3 Prototype Handling

In traditional PRDs prototypes are embedded images; in SPEC they are separate HTML files that AI can parse directly, eliminating the need for visual interpretation.

5. Origin of SPEC

The idea traces back to the 1980s “Design by Contract” concept and the 2004 integration of contract‑first thinking with Test‑Driven Development. The 2025 explosion of AI coding tools (GitHub Copilot, DeepMind Codey, Amazon Cloud AI, etc.) highlighted the need for precise specifications, prompting major vendors to release Spec Kit tools and to promote Spec‑Driven Development.

6. Real‑World Experience

6.1 Dramatic Efficiency Boost

Using a full‑SPEC workflow, the author handled five medium‑size requirements in a single morning—work that previously required a full week (≈32 developer‑hours per requirement).

6.2 How It Was Done

With a pre‑built domain knowledge base, the author opened five AI chat windows, let the AI generate background, confirm a solution sketch, produce the SPEC document and HTML prototype, and only intervened to verify direction and result. The document‑and‑prototype creation step was eliminated, costing about US$50 in model usage but delivering a far higher ROI.

6.3 Freed Time

The saved time allowed deeper judgment on whether a demand is truly valuable, exploring alternative solutions, and assessing business impact.

7. AI Pushes Product Managers Back to the “Judgment Layer”

7.1 Expression Layer Is Automated

AI can now write docs and draw prototypes faster and more consistently than humans.

7.2 Core Value Is Judgment

Product managers must decide the true nature of a demand, the real user pain points, valuable scenarios, boundaries, and rapid validation—tasks AI cannot replace.

7.3 Two Essential Traits

Curiosity to adopt new tools and professionalism (deep business understanding) enable product managers to leverage AI effectively.

8. Caveats and Next Steps

SPEC is not a silver bullet; it requires supporting infrastructure such as a domain knowledge base, skill configurations, and MCP connections. Future articles will cover building knowledge bases and redesigning the entire product‑research‑development chain for AI as the executor.