Why Traditional PRDs Are Obsolete in the AI Era—and How SPEC Docs Supercharge Development

1. The Problem with AI‑Generated PRDs

Many product managers have tried to let AI write requirement documents, only to receive drafts that look decent but contain vague boundaries, logical gaps, and missing edge‑case handling. When presented to developers, these documents require extensive clarification and rework, making the approach slower than manual PRD creation.

2. Three Ways to Produce Requirements

The article compares three mainstream methods:

Traditional PRD

For over a decade, PMs have written PRDs that include background, goals, user stories, feature lists, prototypes, and exception flows. The document’s audience is human, so it relies on developers to infer missing details, making the process flexible but inefficient—often taking one to two days per PRD.

Vibe Coding

Coined by Andrej Karpathy in early 2025, Vibe Coding lets a user describe a desired app in natural language and receive generated code directly from the AI. While suitable for small demos, the lack of explicit constraints leads to unpredictable outputs, especially for complex business requirements, resulting in higher correction effort than writing code manually.

Spec Coding (SPEC‑driven)

Spec Coding, now rapidly adopted in industry, replaces the human‑focused PRD with a SPEC document written for AI consumption. The SPEC contains only the core logic, business flow, boundary conditions, and acceptance criteria, with prototypes linked as external HTML. Developers feed the SPEC to the AI, which then generates executable code without an intermediate “human‑to‑AI” translation step.

3. The Core Shift: Interface from Human to AI

Historically, the downstream consumer of a PRD was a developer who could fill in gaps using experience. Today, developers increasingly let AI generate code from prompts, turning the AI into the execution engine. Consequently, the input format must change from a narrative PRD to a precise SPEC.

Human vs. AI Interpretation

Humans can infer missing information, but AI follows exactly what is written; any ambiguity leads to incomplete or incorrect code. Therefore, a SPEC must lock down all boundaries, error codes, and constraints.

Full‑Chain Impact

When the interface changes, every stage of the product‑development chain—requirement writing, prototyping, review, testing, and deployment—must be redesigned to accommodate AI as the executor.

4. SPEC vs. Structured PRD

Although a SPEC looks simpler because it omits descriptive prose and embedded mockups, its information density is higher: every sentence is a hard requirement for the AI.

Design by Contract Roots

The idea traces back to the 1980s “Design by Contract” concept, later combined with Test‑Driven Development around 2004 to form a “spec‑first” approach.

Industrial Adoption

In late 2025, major AI‑coding platforms (Amazon Cloud, GitHub Copilot, Google DeepMind Codey, Tencent Cloud) all faced low efficiency and high rework when feeding raw prompts to AI. They converged on the solution: write a SPEC first, then let AI generate code. GitHub released a Spec Kit tool, and Thoughtworks highlighted Spec‑Driven Development in its radar.

5. Real‑World Experience

Using a fully SPEC‑driven workflow, the author processed five medium‑size requirements in a single morning—work that previously required a full week (≈32 developer‑hours per requirement). The process involved preparing a domain knowledge base, opening five parallel AI chat windows, and having the AI generate SPECs and HTML prototypes while the author only validated direction and outcomes. The cost was about $50 in model usage, but the labor savings were far greater.

6. The Evolving Role of the Product Manager

AI has taken over the “expression layer” (document writing, prototyping). The true value of a PM now lies in the “judgment layer”: defining the essence of a need, assessing business value, setting boundaries, and designing low‑cost experiments. These capabilities cannot be replaced by AI.

Two core traits become essential:

Curiosity —quickly adopt new tools like SPEC to achieve order‑of‑magnitude productivity gains.

Domain expertise —deep understanding of business scenarios to make sound judgments about what to build.

7. Conclusion

Traditional PRDs are not dead but are becoming an inefficient legacy. The new rule of product management is to identify valuable demands faster, make precise judgments, and leverage AI to execute them. Early adopters of the SPEC interface reap the biggest efficiency dividends.