Vibe coding—building an application by instructing generative AI in natural language—is spreading rapidly. Even departments without engineers can now prototype internal tools, and the conventional wisdom that “you have no choice but to outsource” really is starting to shift.
At the same time, we as a development firm are seeing a new type of inquiry: “We’ve started using an AI-built tool within our department, but we don’t know whether it’s OK to roll it out company-wide as-is.” This article organizes the perspectives for raising a vibe-coded deliverable to a quality that withstands production use.
The typical state of what vibe coding “produces”
AI-generated code runs. But there is distance between “it runs” and “you can operate it.” Here are the states we commonly see.
- Only the happy path is considered — It works on correct input, but breaks on unexpected input, concurrent operations, or a network error
- Security holes — Admin functions with no authentication, hard-coded API keys, unvalidated input (SQL injection, XSS)
- Ad-hoc data design — A structure that falls apart when you add a field later or the record count grows
- No tests — Every fix breaks something else (especially likely when you have the AI make the fix)
- A return of key-person dependency — A state where “the spec exists only in the builder’s chat history.” Ironically, a tool meant to end key-person dependency creates a new one
Pre-production checklist
| Perspective | What to confirm | Priority |
|---|---|---|
| Authentication and permissions | Is it usable by anyone who knows the URL? Do you need to distinguish permission levels? | High |
| Secrets | Are API keys and passwords written directly into the code? | High |
| Data protection | Where personal or confidential data is stored, and its encryption. Whether backups exist | High |
| Error handling | Does data stay intact on failure? Does the user see a comprehensible error? | Medium |
| Load | Does it degrade at the expected number of users and data volume? | Medium |
| Maintainability | Is there documentation of the spec? Can someone other than the builder fix it? | Medium |
If you have any doubt about even one of the three “High” items, we strongly recommend inserting a review before a company-wide rollout. A data breach is a failure you cannot rebuild your way out of.
A realistic path to production quality
- Treat what you have as a “specification” — A vibe-coded deliverable is the finest prototype of your requirements taking shape. There is no need to throw it away. An order of “make it so the same thing can be done, safely” is faster and more accurate than requirement definition from scratch
- Have an expert review it to separate “the parts to rebuild” from “the parts you can keep” — Cases where everything has to be rebuilt are in fact rare; often it is enough to firm up just the authentication and data layers
- Put tests and monitoring in first — Create a state where you’ll notice if something breaks even when you have the AI make later fixes, and only then resume adding features
- Don’t kill the in-house momentum — Having the field iterate on improvements with AI is a major asset. A division of labor where “professionals firm up the foundation, and the field iterates on screens and small improvements” gets you both in-house speed and quality
What to convey when you do outsource
- The tool you built (with the code, if possible) and the key points of your exchanges with the AI
- Who will use it, how many people, and what data it handles (this determines the required security level)
- Whether you want to keep improving it in-house, or entrust operation as well
With these three points, a development firm can estimate “while looking at something that runs,” so the work proceeds faster and with less misalignment than ordinary contract development.
Summary | Toward a division between “building with AI” and “professionals firming it up”
Vibe coding is not the end of outsourcing; it means the shape of the order has changed. Instead of entrusting everything from scratch, you take an AI-built prototype as the foundation and have professionals firm up only the security, data design, and operability—SHANNON offers exactly this form of support, as a team that practices AI-driven development in-house too. Inquiries starting from a state where something already runs are most welcome.