- Describe an idea
- Inspect the result
- Test the assumptions
- Decide what is ready
How does a prompt become an application?
A person describes the desired behavior in ordinary language, and an AI coding tool generates or changes code. The tool may also run commands, inspect errors and revise the implementation. The person then tries the result and gives more direction. The interaction can feel closer to editing a draft than writing each line manually, but the output is still software with dependencies, configuration and behavior that must be understood.
The term is used loosely. Some people mean an exploratory process where they pay little attention to the code. Others use it for any AI-assisted development. For a business decision, ask what the team actually does rather than relying on the label. Does someone review the changes, understand the architecture and verify the important behavior? Those practices matter more than whether the project is described as vibe coding.
Where can this approach be useful?
It can be useful when the main uncertainty is whether an idea or interaction makes sense. A rough scheduling interface, an internal calculator using sample data or a prototype of a customer portal can help a team discuss requirements. Seeing the workflow may reveal that the original request was incomplete or that users need a different path. The value is the learning produced, not simply the amount of code generated.
Keep the experiment bounded. Decide which question it is meant to answer and use data appropriate for that purpose. A prototype intended to compare two navigation approaches does not need real customer records. A demonstration of a quote request does not need to accept payments. Reducing unnecessary functions makes it easier to evaluate the idea and avoids treating a demonstration as an operating product by accident.
What does an original practical example look like?
Imagine a small equipment-rental business exploring a staff tool. In this hypothetical example, the owner asks an AI to show available equipment, create a reservation and display returns due today. The first prototype uses invented equipment and customer names. Staff can click through the screens and identify missing questions: can a reservation overlap, who approves an extension and what happens when an item is returned damaged? This is an illustrative scenario, not a Dappr project.
The prototype has done useful work by exposing those decisions. It has not yet proved that the reservation system is reliable. The next version needs explicit rules for availability, user roles and conflicting changes. A screen that removes an item from the list after a click could still allow another user to reserve it at the same time. The team must test the underlying behavior, not only the visual response.
A sensible review might compare the prototype with a written set of acceptance examples. Two staff members cannot confirm overlapping reservations for the same item. A user without the right role cannot change pricing. A failed connection does not silently lose the request. The examples turn an attractive demonstration into questions that can be verified. They also help decide whether to keep, revise or replace the generated implementation.
Why can an app look finished before it is ready?
Interfaces are easy to observe. Data integrity, access control and recovery behavior are less visible during a quick demonstration. A login screen may exist while the server still exposes records incorrectly. A form may show success before the data is safely stored. A button may work for one user but fail when a session expires. These are ordinary software concerns, regardless of whether the code was written by a person or generated by AI.
The review should therefore include the conditions a demo often avoids. Try an empty account, invalid input, a denied permission, a slow connection and a user with a different role. Check what the application tells the user and what actually happens to the data. The goal is not an endless search for hypothetical problems; it is evidence that the important customer and business actions behave as intended.
What remains the developer's responsibility?
Someone needs to own the technical decisions, review the changes and explain how the system operates. GitHub's responsible-use guidance for generated suggestions emphasizes reviewing and validating output; it also notes that generated tests may not cover all scenarios. An AI tool can assist with implementation and review, but the presence of a tool does not establish that the work has been checked appropriately.
The responsible person should understand the project's structure, dependencies and deployment process well enough to diagnose problems and make changes safely. They should be able to explain which parts are uncertain and what evidence supports release. If nobody can answer how accounts are authorized or where data is stored, more polished screens will not resolve that gap. Ask for an understandable explanation and a concrete verification plan.
Can AI-generated tests prove the generated code is correct?
Tests are useful when they check the required behavior independently of the implementation. A test that simply repeats the code's assumptions can pass while the product is wrong. Start from the user and business rules, then use tests to check the meaningful cases. For the rental example, that includes conflicting reservations and unauthorized changes, not only whether the reservation button renders.
AI can help draft tests and identify cases, but someone should review whether they represent the requirements. Combine appropriate automated checks with inspection of complete workflows. Keep a record of important limitations rather than describing a passing test suite as proof that every possible condition works. The level of verification should reflect what the application does and the consequences of failure.
How should a business handle data and tool access?
Decide what the coding tool needs to see and which actions it may perform. A demonstration often needs much less access than a production integration. Use sample or appropriately prepared data when real records are unnecessary. Keep account credentials and sensitive information out of casual prompts and public project files. The team should understand the selected tool's settings and approved data-handling practices.
If a tool can run commands or connect to services, its permissions are part of the workflow design. Distinguish local experimentation from actions that change live records, publish a site or charge a customer. Those transitions should be deliberate and reviewable. A successful local build is not authorization to connect every production account or release the application to users.
When should a prototype be rebuilt rather than extended?
A prototype is not wasted because some code is replaced. If its structure makes the required behavior difficult to reason about, rebuilding a small part may be cheaper and clearer than accumulating patches. The decision should follow an assessment of the code, data model and requirements. Do not assume generated code must be discarded, and do not assume that visible progress makes every early choice worth preserving.
Separate reusable learning from reusable implementation. The team may keep the validated workflow, content and acceptance examples while replacing the way data is stored or requests are handled. Ask the developer to explain the specific reason for a rebuild and what evidence will show that the replacement solves the problem. A vague claim that the old version is not production quality is not enough to assess the scope.
What should a business ask before paying for a release?
Ask who owns the source, accounts and operating responsibility; what has been tested; which known limitations remain; and how the product will be updated. Request a demonstration of the important failure states as well as the normal path. Confirm that the release scope includes the necessary configuration, monitoring and handover, rather than only code that runs on the developer's machine.
Dappr provides AI-assisted app and custom-development work within a defined scope. The useful promise is a clear process and reviewable deliverables, not that AI removes the need for engineering judgment. Treat prompt-led building as a way to explore and implement ideas while keeping a person accountable for the decisions and evidence needed to operate the result.
Questions before you begin
Is vibe coding the same as no-code development?
Not necessarily. Prompt-led tools may generate conventional source code, while no-code tools typically provide their own visual building model. Inspect what the selected tool produces, how it is hosted and what you can change or export.
Can a nontechnical owner use a prototype to write better requirements?
Yes. Try the workflow, record where it is confusing and describe the expected behavior in concrete examples. Bring those findings to the person responsible for assessing the implementation before a real release.