- Where can generated code help?
- What remains necessary?
- How do you evaluate a provider?
| Responsibility | Prompt-led shortcut to question | Accountable process to require |
|---|---|---|
| Requirements | Are visible screens standing in for real rules? | Document representative workflows and exceptions |
| Code | Is generated output accepted without understanding? | Review decisions and preserve change history |
| Testing | Do checks only repeat the happy path? | Validate independent expected behavior |
| Security | Is a demo being mistaken for verified controls? | Review relevant requirements and evidence |
| Maintenance | Can only the original author repair it? | Deliver understandable code and operating ownership |
Original analysis. Traditional development may use AI; neither label alone establishes quality.
Define the development process behind the label
Vibe coding is used loosely to describe building software through natural-language instructions and AI-generated code, sometimes with limited inspection of the implementation. Traditional development is also a broad label and may include AI assistance. The meaningful comparison is how requirements, review, testing, and maintenance are handled.
Using an AI tool does not automatically make a project careless, and writing every line manually does not automatically make it reliable. A responsible team can use generated code while retaining engineering accountability. The business should ask what evidence supports the finished behavior rather than judging the project by the tool alone.
Consider a fictional community equipment library planning a reservation system. A quick prototype can help volunteers discuss the interface. A live system must also prevent conflicting reservations, enforce permissions, preserve records, and recover from failures. Those responsibilities exist regardless of how the first screen was produced.
Use prototypes to answer a bounded question
A prototype can make an idea concrete before the business invests in a full implementation. The equipment library might compare a calendar view with a simple request form, using fictional records. The useful result is feedback about the task and missing requirements, not proof that the system is ready to manage real inventory.
State what the prototype does and does not implement. A button may display a confirmation without saving a reservation, and a login screen may not enforce access. These shortcuts can be appropriate for a clearly labeled demonstration but become dangerous when the business mistakes visible behavior for complete functionality.
Keep the evaluation focused. Ask whether a volunteer understands how to request an item, what information is missing, and how an administrator handles an exception. Record those findings before adding more screens. Fast generation can otherwise produce a large interface around an unresolved process.
Translate the real workflow into explicit rules
The equipment library needs rules for overlapping requests, pickup and return status, cancellations, and unavailable items. It must decide who can change a reservation and what happens when an item is damaged. A prompt that says build a booking app leaves many of these decisions unstated.
Write representative scenarios in plain language. For example, two people request the same item at nearly the same time, or a volunteer returns it late while another reservation is pending. These cases help the team define the expected behavior and create tests that examine the business rule rather than merely clicking through the happy path.
A traditional discovery process can perform this work, and an AI-assisted process should also perform it. The distinction is not whether requirements appear in a long document. It is whether the team has a shared, reviewable understanding of the decisions that determine correct behavior.
Review generated code as an implementation proposal
GitHub's current guidance for Copilot inline suggestions says users remain responsible for reviewing and validating output. It identifies limitations including inaccurate code and incomplete awareness of larger architectural issues. That is evidence about a named tool, not a universal measurement of every AI system's performance.
For the library, a generated reservation function should be inspected for how it handles concurrent requests and failed writes. A plausible function name and a successful demo do not prove those cases work. Someone must understand the implementation well enough to explain the decisions and correct defects.
Keep changes reviewable and preserve a history of what was altered. If a tool rewrites unrelated behavior while fixing one issue, the team needs to detect that. The business should not depend on repeated prompting as the only way to understand or maintain its own application.
Test behavior independently from the code that produced it
A generated test can repeat the same mistaken assumption as generated implementation. Define expected outcomes from the requirements and inspect whether the tests challenge important boundaries. Useful checks include permissions, duplicate actions, invalid inputs, interrupted requests, and recovery, depending on the application.
The equipment library should verify that a member cannot modify someone else's reservation and that an administrator's change is reflected consistently. It should also test the user experience when a requested item is no longer available. These scenarios explain why a passing build is only one part of validation.
Testing scope should match the consequences of failure. A disposable concept page needs a different level of review from a system holding personal records and operational commitments. The provider should explain what was checked, what remains unverified, and which conditions would prevent a responsible release.
Separate development AI from product AI
An application can be built with AI assistance without containing any AI feature for its users. Conversely, an application written through a conventional process may call an AI service at runtime. These are different decisions with different operating costs, privacy considerations, and failure modes.
The library's reservation system may need no runtime AI at all. Adding an automated recommendation feature would create a separate requirement: what information it uses, what it may suggest, how errors are handled, and what external service costs apply. It should not appear merely because an AI coding tool was used during development.
Keep these costs separate in a proposal. Tool subscriptions for the development team are not the same as hosting, database, messaging, or runtime model usage. A low-cost coding subscription does not establish the total cost of a reliable application or its ongoing support.
Compare total delivery and maintenance effort
AI assistance may accelerate particular drafting or implementation tasks, but this guide establishes no universal percentage saving. Requirements, review, integration, testing, deployment preparation, and maintenance can still dominate the work. A quick first version may require substantial correction before it is suitable for real use.
Ask for a scope-based estimate and the assumptions behind it. Which workflows are included? Which integrations have been verified? Who reviews security-sensitive behavior? What support and documentation are delivered? Compare the same finished responsibilities rather than a prototype price against a production project quote.
For the equipment library, maintenance includes responding to bugs, updating dependencies, and preserving a recoverable record of reservations. The organization needs access to its code and accounts through an appropriate handoff. A system that only the original prompt author can repair creates a continuity problem regardless of development method.
Require security and release evidence appropriate to the application
OWASP's Application Security Verification Standard provides a framework for examining application security requirements. It can help organize a review, but mentioning the standard does not certify the application or prove every requirement has been satisfied. The team needs a defined scope and evidence for the relevant controls.
Before the library uses the system with real members, review access control, handling of personal information, backups, and the release process. Use test data where possible during development. A provider should explain how a failed change can be corrected and how the business will know if an important workflow stops functioning.
Dappr offers app-development services and has a commercial interest in this comparison. Bring the actual workflow, data requirements, and consequences of failure. The useful choice is a development process with accountable review and maintainable results, whether or not AI tools help produce parts of the implementation.
Questions before you begin
Is all AI-assisted development vibe coding?
The term is used inconsistently. Some teams use AI within a disciplined engineering process, while others rely mainly on prompts and visual checks. Ask how requirements, code review, testing, and maintenance work. Those practices are more informative than the label applied to the development method.
Can a fast prototype become the production app?
Sometimes parts can be retained, but the prototype must be evaluated against production requirements. Demonstration shortcuts may not implement real permissions, persistence, or failure handling. Review the architecture and behavior before using it with real data or operational commitments; visual completeness is not release evidence.
Do AI-generated tests prove the code is correct?
No. Tests can reproduce an incorrect assumption or omit important scenarios. Define expected behavior independently from the implementation and check meaningful boundaries. Passing tests are useful evidence only within their scope, and the team should explain what they do not cover.
Does an AI coding tool mean the finished app needs an AI subscription?
Not necessarily. Development assistance and runtime AI are separate. The finished app may have no AI feature at all. If it does call a model service, scope the usage, data handling, limits, and recurring costs independently from the tools used to write the code.
How should I evaluate claims that AI makes development much cheaper?
Ask which tasks are reduced and which finished responsibilities remain included. Compare equivalent requirements, review, testing, integration, documentation, and support. This guide provides no universal savings percentage; a cheap prototype and a maintainable production system are different scopes with different evidence needs.