AI can write code. Building a product takes more.

GitHub’s documentation describes coding agents that can propose changes, run checks, and open pull requests. These capabilities move AI beyond suggesting the next line of code. They let a developer delegate a bounded piece of work and inspect the resulting change.[1]

For a founder, this can make it practical to explore an interface, compare approaches, or develop a narrow feature before committing to a larger plan. The useful unit of progress is a user journey: someone can complete a task and the application behaves as intended. A collection of generated screens does not yet establish that.

Imagine a simple client portal. A useful first version might let a client sign in, upload a document, see its status, and receive a reviewed response. Write those actions down, along with who can see each document and what should happen if an upload fails. This gives both the developer and an AI assistant something concrete to work toward.

A short feature list helps, but acceptance criteria make it testable. ‘Clients can upload files’ leaves important questions open. ‘A signed-in client can upload an allowed file type, receive a clear confirmation, and see only their own files’ describes behavior that can be checked.

GitHub advises reviewing and testing agent-generated code before merging it. Its guidance also makes clear that generated code may contain errors or security issues. These checks belong inside the development process, rather than becoming a final glance before release.[1]

For the client portal, testing the successful upload is one case. Also check the wrong file type, an expired session, a network interruption, and an attempt to access another client’s document. Ask what evidence demonstrates that each requirement works.

• Review permissions and data access for each user role.
• Test the complete journey, including failures and recovery.
• Keep credentials out of source code and browser-delivered code.
• Document the release process, running costs, and rollback path.

Productivity claims need context. METR’s July 2025 study found a 19% slowdown for experienced open-source developers using early-2025 AI tools on their own repositories. This was a particular group, toolset, and type of task, not a verdict on every developer or later model.[2]

In its February 2026 follow-up, METR said selection effects made its newer experiment an unreliable estimate of the current productivity effect. It considered greater speedups plausible but could not confidently measure their size. That is a reason to measure your own delivery process rather than promise a universal multiplier.[3]

Track time to a tested feature, time spent reviewing and fixing it, and defects found after release. An AI-assisted process is useful when it improves these outcomes for your project. Counting generated code or completed prompts cannot tell you that on its own.

Before launch, decide who owns the hosting account, who can release a change, how data is backed up, and who handles a problem. List the third-party services and licenses the application depends on. A product that only one person knows how to run will be difficult to maintain, however quickly its first version was produced.

A useful handover includes setup instructions, a description of the main components, the tests that matter, and a short operating guide. Agree on maintenance separately so the owner understands what happens when a dependency changes or a new feature is needed.

PraoAI Labs can help define and build a first version, add development automation where it fits, and prepare the application for ongoing use. The aim is a product whose behavior, costs, and next steps you can understand.