A widely-read article on Juejin (nearly 100,000 views) surfaces a neglected fact: no matter how many prompts you've learned, most people still just throw "implement this for me" at the AI when writing code. What blocks people was never the difficulty of prompts—it's the lack of a stable workflow.

The three workflows (requirement breakdown, debugging, code review) share a common logic: AI is responsible for expanding the analysis scope, while developers confirm facts, make decisions, and complete acceptance. This is the key shift taking AI coding from "toy demo" to "team-usable."

What This Is

The article breaks a usable workflow into three parts: input context, phased collaboration, and output verification. The three specific workflows are:

Requirement Breakdown: When product throws only one sentence, first have AI list ambiguities, risks, and items to confirm, then break it down into a development task list with complete prompts attached.

Debugging: For production errors, don't just paste the error message. Provide expected results, actual results, key logs, and recent changes. Have AI rank root-cause hypotheses by likelihood, with minimal verification steps for each hypothesis.

Code Review: Don't just ask "is there a problem" with AI-generated code. Instead, check in layers—"functional correctness → permission/security → performance/concurrency → test coverage"—and output graded by "blocking / important / suggestion."

All three workflows share three principles: don't write code first; mark uncertain content as "to be confirmed"; any conclusion must come with evidence and a verification method.

Industry View

Supportive view: Workflow-driven approaches solve "AI coding always fails" better than prompt optimization, especially in team scenarios.

Counterarguments worth noting: First, all three workflows assume models reliably follow requirements, but in real enterprise deployment, models often skip clarification steps and jump straight to answers. Second, for teams of five or fewer, these workflows may be slower than direct communication. Third, long-term reliance on AI to think through problems weakens developers' own requirements-analysis capabilities.

Our judgment: The methods themselves are worth borrowing, but "which workflow fits which task" is the real threshold—something the article doesn't fully address.

Impact on Regular People

For enterprise IT: These three workflows can serve as the seed for internal AI usage standards. We recommend piloting in 1–2 projects first, then consolidating into SOPs (Standard Operating Procedures).

For individual professionals: Even for non-programmers, the mindset of "clarify first, then act; require evidence before concluding" applies to all AI collaboration scenarios.

For the consumer market: AI coding tools (Cursor, Trae, CodeGeeX, etc.) are shifting from "code completion" to "workflow embedding." Future tool selection will depend less on raw intelligence and more on workflow fit.