Hiring a Developer to Rescue AI-Generated Code

There is a recognisable moment in many AI-assisted projects: the prototype worked beautifully for two weeks, and then every new prompt started breaking something that used to work. Authentication loops, a payment flow that half completes, a deploy that fails for reasons nobody can explain. At that point the fastest path forward is usually a human developer who can read the code the tool produced. This guide covers how to hire that person well, from preparing the codebase to judging the first conversation.

Before you write the job post

A developer can only estimate what they can see. Spend an hour getting the project into a state that someone outside your head can understand.

1. Export the code to a Git repository you own. Most AI builders and editors support syncing or exporting to a repository; do it now, not after you hire.
2. List every external service the app touches: database, auth provider, hosting, payments, email, storage, analytics. Note which account each one lives under and who has the login.
3. Write down what is broken, as reproducible steps. "Login doesn't work" is hard to quote. "Signing in with Google on the deployed site returns to the login page with no error; it works on the preview URL" is something a developer can reason about, and that particular symptom often points at redirect URL configuration.
4. Note what the tool changed recently. If you know the last three prompts that preceded the breakage, write them down. Large regenerations are a common cause of regressions.
5. Decide what you will not change. If the design is final, say so. It narrows the work and the estimate.

Writing a brief that attracts the right people

The job post should describe the problem in technical terms, even if you are not technical yourself. Include the stack as best you know it (for example React with Vite, Supabase for database and auth, deployed on Netlify), the specific failures, and what "fixed" means in testable terms.

A useful structure is a short table of issues with acceptance criteria:

Issue | Done when

Google sign-in fails on production | Sign-in works on the live domain in a fresh browser profile

Any logged-in user can read all orders | A second test account cannot read another account's orders via the API

Checkout never marks orders as paid | A test payment updates the order status within a minute

Ask candidates to say which items they would tackle first and why. Their ordering tells you a lot. Someone who puts the data exposure issue at the top understands risk; someone who wants to rewrite the whole frontend first may be solving a different problem than yours.

Where to look and how to shortlist

General freelance marketplaces, developer communities and referrals all work. What matters more than the venue is that you can see evidence of comparable past work. One option is to hire freelancers on Workli, where finishing software built with Lovable, Bolt, Replit, v0 and Cursor is a dedicated hiring category, and each job gets a shortlist ranked by how many of the job's required skills each freelancer lists, which is a transparent count rather than an opaque score. Workli is a newer and smaller marketplace than the big names, so treat it as one channel among several rather than your only option.

Whatever the channel, look for:

• Past work involving the same backend you use. Supabase row level security, Firebase security rules and custom Node APIs are different skills.
• Experience reading code they did not write. Ask directly how they approach an unfamiliar codebase.
• Comfort saying "this part should be rebuilt" with a reason, rather than either refusing to touch generated code or promising to fix everything by patching.

The first call: questions that reveal real experience

Keep the call practical. Share the repository read-only beforehand if you are comfortable doing so, and ask:

1. "After looking at the code, what worries you most?" A strong candidate names specific files or patterns.
2. "How would you confirm the data exposure issue is fixed?" Look for an answer involving a second test account or direct API requests, not "I'll check the code".
3. "What would you need access to, and when?" They should ask for the minimum, and ideally suggest invited collaborator access rather than your personal passwords.
4. "How would you split this into milestones?" You want small, dated pieces with something testable at the end of each.

Be wary of fixed promises made before the candidate has seen the code, and equally wary of anyone who insists the only option is a full rewrite without explaining what in the current code makes it unsalvageable.

After you hire

Start with a small, well-defined first milestone, ideally the highest-risk issue. Give access through collaborator invites on GitHub, your hosting provider and your database dashboard, so it can be revoked cleanly later. Ask for changes to arrive as pull requests with a short description, even if you cannot review every line; the history is valuable for whoever works on the project next.

Rescuing AI-generated code is a normal, well-understood kind of engineering work now. The projects that go smoothly are the ones where the client did the preparation above: code in a repository, services listed, failures written as reproducible steps. Do that, and you will get better estimates, better candidates and a much shorter path to launch.