How to Choose an Engineer to Stabilize an AI App Without Rebuilding Everything
Short answer: hire a senior production engineer with real LLM experience whose first move is a written audit, not a rewrite proposal. The right person asks "how do we make this safer and more predictable while keeping what already works?" They trace failures end to end, add observability so you can see problems, and fix them in place.
Most AI apps that feel broken are not beyond saving. The screens, flows, and data model usually work. The failures cluster in error handling, prompts and retrieval, auth, secrets, and missing monitoring.
What to look for
| Capability | Why it matters | How to check |
|---|---|---|
| Production debugging | Failures span your app, model APIs, retrieval, queues, and vendors | Ask how they'd trace one intermittent failure end to end |
| AI observability | You can't fix what you can't see | Ask what they'd log for each LLM call (input, output, cost, latency) |
| Evals | Stops fixes from silently breaking other cases | Ask how they'd build a test set from your real traffic |
| Incremental change | Keeps the app running while it gets fixed | Ask for their order of operations in the first two weeks |
| Security basics | AI-built apps often leak secrets or skip authorization | Ask what they check first in an AI-generated codebase |
| Cost awareness | Runaway token bills are a common "it's broken" symptom | Ask how they'd cut cost per request without hurting quality |
How to spot a rewrite-first engineer
- Proposes a new stack before reading your code
- Estimates the rebuild in the first call
- Talks about the framework they prefer rather than your failures
- Can't name what is working in your current app
A rebuild can be the right answer, but it should come out of an audit, with evidence, not out of the sales call.
Questions to ask
- "What will you do in the first three days?" (Good answer: read the code, reproduce the top failures, check logs, write findings down.)
- "How will we know the app is more stable, in numbers?"
- "What would make you recommend a rebuild instead?"
- "How do you make changes without breaking what works?"
- "What will my team be able to maintain after you leave?"
What a good engagement delivers
- A written audit with prioritized issues
- Fixes for the top production blockers
- Logging and tracing on every model call
- An eval set that runs on each change
- Alerts, so you hear about failures before customers do
- Documentation and a handover session
I run this as a 3-day audit followed by targeted fixes or a 14-day reliability sprint. Here is what each costs: cost to audit and repair a broken AI MVP.
Need an AI app stabilized, not rewritten?
I'm Rajesh Dhiman, an AI systems engineer based in India who rescues AI-built and vibe-coded apps for founders worldwide. See AI code rescue or read what AI code rescue involves.
Frequently asked questions
How do I choose an engineer to stabilize an AI application without rebuilding everything?
Look for a senior production engineer with LLM experience whose first step is a written audit, not a rewrite proposal. They should be able to trace failures across your app, model calls, retrieval, and third-party services, add observability and evals, and fix problems in place while keeping what already works.
Should I rebuild or stabilize my AI app?
Stabilize first in most cases. Rebuilds take longer than expected, throw away working parts, and repeat old mistakes. A rebuild is justified when an audit shows the core can't be made safe, for example a data model that can't separate one customer's data from another's.
What should an AI app stabilization engagement deliver?
A written audit with prioritized issues, fixes for the top production blockers, logging and tracing on every model call, an eval set that runs on each change, alerts, and documentation your team can maintain.
Vibe Coding Survival Guide
How to Ship AI-Assisted Code That Doesn't Embarrass You in Production
A production-minded guide for developers, founders, and teams using AI coding tools without letting brittle generated code reach customers.
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