- Define the task and boundaries
- Build a reviewable increment
- Test behavior independently
- Handover the operating product
Start with a problem worth implementing
Describe the current process and what the user should be able to do differently. A staff member might need fewer repeated entries, a customer might need a clearer request status or a manager might need an understandable view of approved records. Those are outcomes that can be discussed and tested. The phrase build it with AI is not a complete product requirement.
For a hypothetical Salt Lake City service company, the first useful release might track a request through defined stages. A hypothetical professional practice might need a simple internal intake review. A hypothetical membership business could need an account-based information task. These are illustrative possibilities, not Dappr clients or claims that sensitive workflows can be implemented without further assessment.
Separate development assistance from an AI product feature
An AI coding tool may help draft code, explain a component or suggest test cases. A customer-facing AI feature introduces additional questions about model behavior, data, evaluation and fallback. Do not treat those two uses as interchangeable in a proposal. A conventional app can be developed with assistance without depending on a language model during everyday use.
If the business wants the app itself to generate text, classify information or recommend an action, scope that behavior explicitly. Identify what it may get wrong and who reviews consequential output. Confirm feasibility and the supporting service before promising the feature. The current offering on this page is AI-assisted development, not a blanket claim that every AI product requirement is supported.
This distinction also affects costs and maintenance. Development tooling belongs to the delivery process, while a runtime model may create an ongoing service dependency. The business should understand which subscriptions and usage charges apply to the actual product rather than assuming AI makes all software inexpensive to operate.
Use local business context to define real constraints
Salt Lake City's business-development resources provide support information for companies operating in the city. They can help an owner identify relevant business contacts and planning resources. They do not establish demand for a particular app or replace research with the people who would use it.
For a hypothetical business serving customers at a physical Salt Lake City location, the app may need accurate appointment or collection information. A remote professional service may instead prioritize account access and response status. Confirm the actual process and data owner. Local references should clarify the use case rather than create a false impression of a Dappr office or municipal partnership.
Bring constraints into the brief early: who can approve changes, which system already holds the data and how the business handles an incomplete request. An assisted coding workflow can produce plausible screens quickly, but it cannot independently establish those facts about your operation.
Keep generated work reviewable
Define a small change with an expected result before implementing it. Review the changed behavior and the code that supports it. Keep a record of important decisions so the next developer can understand why the system works that way. A large volume of generated code is not evidence of product progress if nobody can verify its purpose.
GitHub's responsible-use documentation for Copilot explains that generated explanations and code can be inaccurate and that generated tests may miss scenarios. That is a primary-source example of why human review and testing remain necessary. It is not a claim that Dappr uses a specific tool, subscription or provider for every project.
Check proposed dependencies and unfamiliar interfaces against current documentation. Do not accept a library or API merely because an assistant suggested it. Review access controls, validation and error handling with particular care. Name any specialist assessment the actual data or use case requires instead of declaring the system secure from a successful demo.
Test the requirement independently of the implementation
A test should examine what the product must do, including cases where it should refuse or recover. If an assistant creates both the feature and a test that repeats the same mistaken assumption, a passing result may be misleading. Use the agreed business rules and realistic user tasks to decide what needs verification.
For a hypothetical request app, check an incomplete submission, an unauthorized user and a repeated action. For a staff tool, verify role changes and the state seen by another user. Use safe representative data and inspect the actual stored result. A screenshot of a confirmation message does not prove the business received the intended record.
Include mobile behavior, accessibility and interruption where relevant. If the app is distributed through Apple or Google, current store requirements and device testing remain part of delivery. AI assistance does not bypass external review or guarantee an approval date.
Control the information used during development
Identify what source code, documents and data may be shared with development tools. Confirm the actual provider settings and contractual requirements before exposing confidential material. Use synthetic or appropriately prepared examples when real customer records are unnecessary. Keep credentials out of prompts and ordinary project files.
The business should know who has access to the development environment and production systems. Separate test activity from live actions that send messages, change orders or affect customers. A working prototype does not authorize it to operate on real records. These boundaries belong in the project process and the implementation.
Plan the handover as a software deliverable
Agree on ownership of source code, accounts and deployment arrangements. Include build instructions, required configuration and the dependencies the product needs. Document the support route and the person responsible for maintenance. The receiving team should not need to reconstruct the system from an assistant conversation.
NIST's Secure Software Development Framework provides high-level practices for integrating security into a software lifecycle. Referencing that framework here is not a Dappr certification or a claim that an app has passed a formal assessment. The practical expectation is that review, verification and maintenance are considered throughout delivery, with the appropriate scope agreed for the project.
Discuss AI-assisted development with Dappr
Bring the task, intended users, current systems and the information the app will handle. Dappr serves Salt Lake City remotely from its staffed St. George office. AI-assisted development, iOS, Android and React Native are confirmed capabilities. The technical approach should fit the task and the team that will operate the result.
A proposal should separate discovery, prototype work, implementation, integrations, testing and maintenance. Review the plans for general context and request a project-specific scope. No fixed speedup, cost saving, platform credential or invented result is promised.
Define the software requirement independently of the development assistant and identify the checks that will demonstrate correct behavior. Use that example to compare the proposed deliverables, review responsibilities and acceptance evidence. Dappr can coordinate the project remotely from its staffed St. George office. Ask the proposal to identify what is included, what depends on another provider and what needs investigation before a commitment. Keep account ownership, maintenance and the staff handover explicit so the result remains usable after the initial build. A project-specific estimate should follow those requirements rather than assumptions about a city or platform.
Questions before you begin
Does AI-assisted development mean the app contains AI?
No. It can describe assistance used during coding and other development work. A runtime AI feature needs its own requirements, evaluation, data handling and operating-cost discussion.
Can generated code be accepted without review?
It should be checked against the requirements and tested appropriately. AI output can be inaccurate or incomplete, and a plausible explanation is not evidence that the implementation works.
Will AI make the project a fixed percentage cheaper?
No reliable percentage can be promised without the actual scope. Discovery, integration, testing, review and maintenance remain necessary work, even when tools assist implementation.
What data should we provide for a prototype?
Provide the minimum information needed to represent the task, using synthetic or appropriately prepared examples where possible. Agree on handling requirements before sharing confidential records or production access.
What should we receive at handover?
The agreed source and account ownership, build and operating instructions, dependencies, test evidence and support responsibilities. A finished product should be maintainable without depending on an undocumented coding conversation.