What this is
Over the past year we've seen too many AI coding tutorials, with 90% of them covering how to use Cursor and Copilot. A recent Juejin article—the seventh installment of AI Coding in Practice—takes a different tack: the first six pieces covered tools and methods, but the seventh pulls the focus back to something more fundamental—code written by AI cannot be used as-is.
The three principles the article lays out aren't complex. First, treat AI as an assistant rather than an answer machine—AI can only produce drafts and suggestions, while business judgment, security accountability, and acceptance/delivery still rest with humans. Second, when asking questions, specify four things clearly—context, goal, constraints, and output format—which works better than any prompt template. Third, all AI output must pass two layers of verification—whether the code runs is one thing, whether the results are correct is another.
The real signal behind these three principles is the working consensus the industry has built up over a year: AI coding is no longer a tool problem—it's a workflow problem.
Industry view
Supportive voices point out that the three principles line up perfectly with what companies like Cursor, Anthropic, and GitHub have stressed on stage all year: "AI is the copilot, not the autopilot." This is the signal that the developer community's understanding has finally caught up with the vendors' messaging.
But the cooler voices are worth hearing. These three principles sound plain, but executing each one carries organizational cost: treating AI code as drafts means keeping code review in place; mandatory two-layer verification means testing roles actually get busier in the short term. The "AI coding cuts costs" story sounds sexy, but implementing it is paying down a debt.
Every decision-maker evaluating AI coding procurement should read this judgment carefully: tool procurement is only the entry point; the real costs hit during process transformation and rebuilding team capability.
Impact on regular people
For enterprise IT: after purchasing AI coding tools, budget priorities will shift from licenses to building code review and testing workflows—CIOs should carve out a dedicated line item for the latter.
For individual careers: non-programmer white-collar workers can also benefit from these three principles by transferring them to everyday AI collaboration—don't treat AI as an all-purpose assistant, articulate requirements clearly, and review every output yourself.
For the consumer market: the biggest barrier to consumerizing AI coding tools isn't the subscription price—it's whether users have the basic ability to "understand, modify, and verify." The future winners are most likely not the companies with the strongest tools, but the companies with the most complete teaching.