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“Add password reset to this existing service” gives an AI assistant a codebase, users, and an established behavior to extend. “Build me an account-management app” leaves much more to define: its interface, data flow, backend, deployment, and ongoing operation. AI can help with either request, but the work of connecting and validating the result grows with the scope.
What separates a feature from an app?
A feature is a change within an existing product. Its repository, conventions, interfaces, and expected behavior provide context. The goal is to make a bounded change that fits those existing constraints.
An app is a connected product. It may need a user interface, backend services, data handling, and AI flows, plus the infrastructure and operational processes needed to deploy and maintain it. Google’s app-prototyping description covers UI, backend code, and AI flows; its application-lifecycle description also includes infrastructure design, deployment, monitoring, troubleshooting, and ongoing optimization. These are documented capabilities and product descriptions, not independent evidence that an AI-generated app will work reliably.
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How the work changes as scope grows
| Dimension | Feature in an existing product | App built as a connected product |
|---|---|---|
| Context and scope | Starts with an existing repository, issue, conventions, interfaces, and expected behavior. | Requires defining users, requirements, system boundaries, architecture, and data flows. |
| Integration boundaries | Must fit the existing code and preserve relevant behavior elsewhere in the product. | Must connect components such as the UI, backend, data handling, and any AI flows. |
| Validation and operation | Review the change, run relevant tests, and check for regressions before following the team’s normal merge process. | Test complete user journeys, assess security and privacy, and plan deployment, monitoring, troubleshooting, and maintenance. |
How to use AI for a feature change
Keep the request narrow and anchored in the existing product. GitHub documents a workflow that can start from an issue or repository, assign an agent, and then review the pull request and continue in an IDE. Google’s IDE documentation describes generated changes in a diff view developers can accept or reject. These are workflow capabilities, not a guarantee that a change is correct.
- Define expected behavior. Describe what users should be able to do and any constraints, including cases the change must not break.
- Provide relevant context. Point to the issue, files, interfaces, and conventions the change should follow. Avoid asking for a broad rewrite when a limited change will meet the need.
- Review the diff. Check every changed file and consider whether the change affects behavior beyond the edited line or file.
- Run relevant tests. Use targeted tests for the feature and check related existing behavior for regressions.
- Review security and data handling. Consider what information the change reads, stores, or exposes, then use the team’s normal review and merge process.
How to use AI when building an app
An app needs more than code that compiles: its parts must work together for actual users, and the product must be operable after it is built. Google describes app-prototyping capabilities spanning UI, backend code, and AI flows, as well as lifecycle support for testing and operations. The steps below are practical guidance based on those connected components and lifecycle concerns, not a checklist prescribed by one vendor.
- Clarify users and requirements. Identify who will use the app, what they need to accomplish, and which behaviors are in scope.
- Define architecture and data boundaries. Decide how the interface, backend, data stores, and any AI flows relate, and what information may pass between them.
- Build connected pieces incrementally. Develop the interface and services in reviewable steps, checking that each component works with the others rather than treating generated code as a finished product.
- Test end-to-end journeys. Exercise complete user tasks across the components, including failure paths, instead of relying only on compilation or isolated component tests.
- Review security and privacy. Examine data access, storage, and handling throughout the product, not just in the code most recently generated.
- Plan deployment and operations. Establish how the app will be deployed, monitored, troubleshot, and maintained after launch.
Security applies at both scopes
AI assistance does not remove the need for a secure development process. Google’s Gemini Code Assist documentation says prompts, responses, and contextual file snippets can be processed, and that Google does not use customer data to train models without permission. Review the terms and settings that apply to your specific product and account before sharing sensitive information.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose review effort by system boundaries
A useful rule of thumb is to look at how many components, users, data stores, and operational boundaries a request crosses. A change confined to an existing interface and service still needs review and tests; a product spanning several components needs more attention to integration, end-to-end journeys, security, and operations. Ask AI for smaller, reviewable steps as those boundaries multiply.
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