- Make the desired behavior explicit
- Review generated work as software
- Choose an appropriate first use
Make the desired behavior explicit
Describe one user task and its acceptance criteria before generating more features. Identify who can access the task, what data is involved and what should happen when it fails. This gives review something firmer than whether the screen looks convincing.
Keep changes small enough to inspect. A generated feature can introduce dependencies or assumptions that are not visible in its interface. Understanding those choices matters when the application later needs repair.
Review generated work as software
Check authorization, input handling, secret storage and error paths. Test with users who should not have access as well as users who should. A tool producing functioning code does not mean the code is safe for real customer information.
Confirm that the application can be built from its repository and deployed through an understood process. Record external services and ownership. Hidden dependencies on a single personal account make a handoff fragile.
Choose an appropriate first use
A prototype can help test a workflow without being represented as production-ready. A public application needs a separate readiness assessment and operating plan. Be explicit about which kind of artifact the project is creating.
Dappr uses AI-assisted development and can scope a build or review of existing generated work. Bring the repository, intended users and current limitations. The conversation should establish responsibility for the finished product, not promise that prompting alone eliminates engineering work.
Make the build method serve a specific product
Vibe coding is often used to describe building software through iterative natural-language instructions to an AI tool. The term does not define a quality standard or remove the need to understand the resulting application. A project still needs clear users, rules, and evidence that important behavior works.
Consider a fictional studio coordinating shared equipment reservations. An AI-assisted prototype might quickly show a calendar and a booking form. The useful question is whether it prevents conflicting reservations and handles changes correctly. This is a hypothetical workflow, not a Dappr client result or a claim about a particular generator's performance.
Break the work into reviewable decisions
Before requesting another generated feature, state the behavior being changed and how it will be checked. For the studio, adding cancellation requires a decision about who can cancel, whether equipment becomes available immediately, and what other users see. Those rules need business approval rather than an assumption invented by the coding tool.
Keep a record of important decisions alongside the implementation. If a generated revision changes a rule that was already accepted, the reviewer should be able to notice. Small, understandable changes make it easier to connect the code with the intended behavior and to reverse a mistaken direction without losing unrelated work.
Inspect dependencies and generated assumptions
GitHub's Copilot inline-suggestion guidance describes limitations including inaccurate code and the need for review and testing. That is documentation for the named tool, not a universal claim about every AI development product. The project should examine the actual output, libraries, and service requirements introduced by its chosen workflow.
For the fictional studio, a generated calendar may depend on a paid service or store records only in a local browser. Either could be acceptable in a demonstration but unsuitable for the intended shared application. Identify the persistence, account, and licensing assumptions before the prototype is presented as an operating product.
Test the rules rather than only the screen
Use cases that distinguish a real reservation system from a visual calendar. Have two authorized users attempt conflicting bookings, change a reservation, and revisit the shared record. Check the underlying outcome, not only the text displayed after a button is pressed.
Also test a user who should not be allowed to modify another person's reservation. A hidden control does not establish that the backend enforces access. The appropriate security review depends on the application, with relevant web controls scoped through a framework such as OWASP ASVS. No tool choice or framework mention certifies the result automatically.
Separate prototype shortcuts from production decisions
A prototype may intentionally use fictional data, simplified permissions, or a simulated notification. Label those boundaries so the business can evaluate the idea without assuming the implementation is ready for real users. Maintain a list of what must change before the intended production use is permitted.
For the studio, the first demonstration might validate whether staff understand the reservation flow. A later operating pilot would need the agreed records, access rules, recovery behavior, and support. These are different acceptance stages. Fast iteration can help explore an idea, but it should not blur the line between an experiment and a maintained business system.
Leave a reproducible handoff
The completed scope should identify the source repository, setup instructions, private configuration management, and deployment process. A future maintainer needs to build and inspect the application without reconstructing a long prompt history or depending on one person's unexplained local environment.
Dappr can discuss AI-assisted builds from a defined workflow or existing prototype. Bring the intended use, generated files, platform details, and known limitations. The proposal should make review, implementation, and operating responsibilities explicit. No fixed speed, cost saving, or production-readiness claim follows merely from describing the work as vibe coding.
Questions before you begin
What does vibe coding mean in a project scope?
The term usually describes iterative AI-assisted implementation through natural-language instructions, but it does not specify deliverables or quality. The agreement still needs to define the workflow, review, testing, and handoff. Ask what the finished product will demonstrably do rather than relying on the label.
Can a generated prototype become a production application?
Potentially, after assessment of the actual code, dependencies, data handling, and intended use. Some parts may be reusable and others may need replacement. Identify simulated behavior and prototype shortcuts before estimating the work required for an operating release.
Does AI-generated code need different acceptance criteria?
The business behavior still needs to be verified against clear requirements. Review generated assumptions and dependencies, then test important normal, failure, and access cases. A convincing interface is not enough evidence that the underlying application follows the agreed rules.
What should I keep from an AI-assisted build?
Keep the agreed source, meaningful decisions, setup and release instructions, and account ownership records. Preserve private settings through an appropriate secure arrangement rather than in public documentation. A usable handoff should not depend only on access to the original prompt conversation.