An AI-built prototype changes the hiring problem. You are not starting from a blank brief, but the visible product may hide important unknowns: duplicated logic, broad database permissions, missing tests, temporary accounts, fragile deployment steps, or an architecture nobody has explained.

The right developer should preserve the useful product learning while reducing a specific production risk. That could mean reviewing access control, preparing a launch, stabilizing payments, improving mobile behavior, setting up monitoring, or planning a migration. “Make it production ready” is too broad to price or verify.

Create an evidence packet. It does not need to be elegant, but it should let a candidate understand both the product and the system without guessing.

Remove credentials from the packet. Share secrets only through the proper service or password manager after access is justified. If customer data exists, use a safe preview environment or sanitized fixtures for evaluation.

Choose the gap with the highest combination of consequence, uncertainty, and leverage. For example:

| Situation | First useful engagement | Verifiable output | | --- | --- | --- | | Real users will create accounts | Access-control and data-boundary review | Findings, fixes, and two-account tests | | Launch depends on one fragile flow | Production-readiness milestone | Test plan, monitoring, rollback, runbook | | AI or API costs are unpredictable | Usage and abuse-control review | Limits, alerts, budgets, failure behavior | | The prototype is hard to change | Architecture and handoff diagnosis | System map and prioritized refactor plan | | A feature blocks customer learning | Narrow delivery milestone | Accepted behavior with tests and deploy |

A paid diagnostic is often the safest first milestone. The developer inspects the real repository and deployment, identifies material gaps, and proposes sequenced work with assumptions. You learn how they reason before granting a large engagement.

Ask each candidate to explain what they would inspect first and why. Strong answers distinguish product evidence from technical evidence. They should ask about users, data sensitivity, permissions, deployment ownership, incidents, backups, recurring costs, and the next business decision—not only the preferred framework.

Use questions such as:

Do not use a free speculative rewrite as the main evaluation. A short paid task creates better evidence about communication, repository care, test discipline, and handoff quality.

Not by default. Rebuild only when evidence shows that the current path cannot meet the requirement at reasonable risk or cost. A working product contains valuable decisions about users, workflows, language, and integrations even if parts of the implementation need change.

Ask for a written disposition by area: keep, harden, refactor, replace, or investigate. Each replacement should name the failure it solves and the migration consequence. “I would never use this stack” is a preference, not a diagnosis.

Likewise, “the app already works” is not proof that no technical work is needed. The decision should come from inspection of the repository, services, data, permissions, tests, and operating history.

Keep the source repository, domain, cloud services, billing, and data under accounts the product owner controls. Invite the developer with the least privilege needed for the milestone. Avoid sharing a single personal login. Use separate production and preview access, short-lived credentials where possible, and an offboarding checklist.

GitHub's CODEOWNERS feature can automatically request review from people responsible for specified paths, but it does not replace repository permissions or branch protection. For a small project, the underlying lesson is simple: make responsibility visible and require review where a change has material consequences.

At the end of the milestone, require merged code, tests, deployment notes, configuration changes, migration instructions, known limitations, and a short walkthrough. Ownership means the project can continue without relying on undocumented memory.

Define the current state, desired user-visible outcome, evidence of completion, exclusions, access, timeline, review process, and handoff. For example: “Ensure authenticated users can create and edit only their own workspaces. Completion requires automated permission tests for two accounts, a migration plan for existing records, deployed verification in preview, and documentation of remaining admin access. Payment and UI redesign are excluded.”

That scope is estimable because the result can be inspected. It also leaves room for a diagnostic finding: if the current data model cannot support the boundary safely, the developer can show why and revise the plan before a risky migration.

The NIST Secure Software Development Framework provides a common vocabulary for preparing the organization, protecting software, producing well-secured releases, and responding to vulnerabilities. The OWASP ASVS provides testable security requirements for web applications. Neither document selects a developer for you, but both help turn “make it secure” into inspectable work.

For higher-risk systems, ask how the candidate uses standards proportionately. Copying a long checklist without understanding the product is not enough; ignoring verification because the team is small is not enough either.

We started from the specific Heyviber handoff: an owner already has a working AI-built prototype and needs focused help without discarding product context. We combined repository ownership practices from GitHub with verification language from NIST SSDF and OWASP ASVS. Cost figures are intentionally absent because a responsible estimate needs attributable scope and current market inputs.

For a real handoff, have a named developer or technical lead review the packet, questions, access model, and milestone example. Add a real anonymized scope and estimate range only with permission and clearly stated assumptions.

Write the evidence packet and select one bounded risk. Browse approved developer profiles for relevant experience, then ask for a paid diagnosis or small milestone with explicit acceptance criteria. Compare the quality of the questions and proposed verification—not just an hourly rate.

The goal is not to find someone who promises to replace AI-generated code. It is to find someone who can understand the product, reduce the next material risk, and leave you with more control than you had before.