AI 지원 소프트웨어 빌드에서 "준비"가 의미하는 것

Readiness rule Are readiness criteria clear enough for launch with AI-assisted coding? The call Ready is a responsibility call, not a polish milestone. vibeCoding and/or AI-assisted coding can produce quick drafts in an application, but the value comes from human-in-the-loop guidance on what is safe to release. Why it matters What ready actually means is that users can rely on the core path when it matters most. AI can speed up analysis and drafts, but a human guiding the final readiness decision keeps quality tied to real risk and real accountability. That human guidance makes the difference between launching something that looks complete and launching something users can trust. Explainer Ready is not a vibe. It is a release boundary that names what must be true before real users arrive. Until the team can name one release boundary, one pass check and one owner for unresolved risk, readiness stays subjective. AI can help run checks, but it cannot decide what is safe enough to ship. Make the readiness rule concrete Compare the broad version with the version a team can actually test. - Too vague: We are almost ready to launch. - Concrete enough to test: We are ready when first-time setup passes on production data, the support fallback is written and one owner is watching the first release window for failures. The readiness rule must be specific enough that two people would make the same ship or hold call from it. Stop when the readiness rule is still soft Do not move into launch or release work until the readiness rule is clear. - Do not proceed: If you cannot name one release boundary, one pass check and one owner for unresolved risk, stop here. - Define it first. The readiness rule must be specific enough that two people would make the same ship or hold call from it. Check the readiness rule Use this as a pass or fail check before moving forward. - Pass: You can say what has to be true, how it will be checked and who owns any remaining risk. - Fail: If ready still means close, almost there or good enough without a check, it is not clear enough yet. How to use AI for the readiness rule Use each mode differently here so the readiness rule gets sharper instead of blurrier. - AI chat: Use it to rewrite the readiness rule until the team can say what has to be true, how it will be checked and who owns any remaining risk. - vibeCoding: Use it to build the thinnest flow that tests this readiness rule in practice before broader build work. - AI-assisted coding: Use it to carry the same readiness rule into implementation, instrumentation and review so the live system keeps the same decision. Sharpen the readiness rule Copy this prompt into AI chat, replace the bracketed lines with your real readiness rule and keep the instruction exactly as visible here. Evaluation Before you accept the result, stop and check whether it would help two people make the same next decision from this readiness rule. It should stay grounded in the same situation and make clear why this version now meets the bar. Example For example: We are ready when first-time setup passes on production data, the support fallback is written and one owner is watching the first release window for failures. Risk and mitigation Name the downside clearly, then pair it with the action that keeps the work under control. - Risk: declaring ready based on polish instead of reliability, which can create trust loss when users hit preventable failures - Mitigation: Defining ready with evidence, assigning ownership for unresolved gaps and using the checklist below before release Key takeaway Decision rule: do not move forward until you can say what has to be true, how it will be checked and who owns any remaining risk. What do you think? How are you defining what ready actually means in your launch work and how is AI helping you make that readiness decision with confidence?