You Don't Need to Read Every Line
If you are a non-technical founder using AI to build your product, you face a genuine dilemma: how do you evaluate code you did not write and do not fully understand?
The answer is not "learn to code" — that takes years. The answer is learning to evaluate code the same way you evaluate anything else in your business: by asking the right questions and looking for specific signals.
Here is a practical checklist that any founder can use.
The Structure Check
Before looking at any specific code, evaluate the overall structure:
Are files organized logically?
Good code is organized like a well-run office — related things are near each other, and the naming makes it obvious what each area does. If you see files named utils.js, helpers.js, misc.js, or a single file with thousands of lines, that is a red flag.
Are there tests?
This is the single most important quality signal. If AI generated features but no tests, the code is unreliable. Tests are how you verify that the code does what it claims to do.
Ask AI: "How many test files exist? What percentage of the codebase do they cover?"
Is there documentation?
At minimum, there should be a README that explains how to run the project and what the main components do. If a new developer could not figure out how to start the application from the documentation alone, that is a problem.
The Dependency Check
Dependencies are external libraries your code relies on. They are both a feature (you do not have to build everything) and a risk (you depend on someone else's code).
Are there too many dependencies?
Every dependency is a potential security vulnerability, a maintenance burden, and a point of failure. AI tends to install packages liberally. Check whether each dependency is actually necessary.
Ask AI: "List all external dependencies and explain what each one does. Are any of these unnecessary?"
Are dependencies up to date?
Outdated dependencies often have known security vulnerabilities. AI may install versions that were current when it was trained, not the latest ones.
Are there any deprecated packages?
Packages that are no longer maintained are ticking time bombs. They will eventually become incompatible with other parts of your system.
The Security Check
You do not need to be a security expert to catch the most common issues.
Are secrets hardcoded?
Search the codebase for strings that look like API keys, passwords, or tokens. They should never be in the code — they should be in environment variables.
Ask AI: "Scan this codebase for any hardcoded secrets, API keys, passwords, or tokens."
Is user input validated?
Anywhere your application accepts input from users — forms, URLs, API requests — that input must be validated. Unvalidated input is how most security breaches happen.
Are there authentication checks?
Every page or API endpoint that should be restricted to logged-in users needs an explicit authentication check. It is common for AI to generate endpoints that work but skip the authentication guard.
The Performance Check
Performance issues are invisible until you have real users.
How does the database handle growth?
Ask AI: "How will this database schema perform with one hundred thousand users? Are there missing indexes?"
The answer should be specific. If the AI says "it should be fine," that is not an answer — push for specifics.
Are there obvious bottlenecks?
Common bottlenecks in AI-generated code:
- Loading entire datasets into memory instead of paginating
- Making API calls inside loops (N+1 queries)
- Missing caching for data that does not change frequently
- Synchronous operations that should be asynchronous
Does the application handle concurrent users?
Ask AI: "What happens when a hundred users access this simultaneously? Are there race conditions or resource conflicts?"
The Error Handling Check
How your application handles errors determines whether users see a graceful message or a crashed application.
Do errors produce useful messages?
When something goes wrong, does the user see a helpful message ("Unable to save — please try again") or a technical error ("TypeError: Cannot read property 'id' of undefined")?
Do errors get logged?
When errors happen in production, you need to know about them. Check that errors are logged to a monitoring service, not just printed to the console.
Does the application recover from errors?
A single failed API call should not crash the entire application. Check that errors are caught and handled at appropriate levels.
The Maintainability Check
The true cost of code is not writing it — it is maintaining it over time.
Could another developer understand this?
Imagine hiring a developer six months from now. Could they look at this codebase and understand how it works without talking to you? If not, the code needs better naming, structure, or documentation.
Is the code consistent?
AI sometimes generates code in different styles across different sessions. The entire codebase should follow consistent patterns for naming, formatting, and organization.
Is there unnecessary complexity?
AI sometimes over-engineers solutions. If a simple feature has an elaborate architecture with multiple abstraction layers, it might be adding complexity without value.
Ask AI: "Are there any parts of this codebase that are more complex than they need to be?"
The Red Flag Summary
Stop and get expert help if you find any of these:
- No tests at all
- Hardcoded secrets in the code
- No error handling on user-facing features
- Database queries that do not use indexes
- Authentication that can be bypassed
- Dependencies with known security vulnerabilities
These are not minor issues — they are the kind of problems that cause data breaches, downtime, and customer loss.
When to Get a Professional Review
Use this checklist for ongoing development, but get a professional code review at two key moments:
- Before launching to real users. The stakes increase dramatically when real people and real data are involved.
- Before raising funding. Technical due diligence is standard in fundraising, and investors will find these issues.
A professional review costs a fraction of what it costs to fix problems after they affect users.
FAQ
How long should this evaluation take?
For a small to medium codebase, about an hour using AI to assist with the checks. The security and performance checks are the most important and should not be rushed.
Can I use AI to evaluate AI-generated code?
Yes, and you should. Ask a different AI model (or a fresh session) to review the code. The reviewing AI will catch issues that the generating AI missed because it brings a different perspective.
What if I find issues but cannot fix them myself?
Document the issues clearly and bring in a developer to fix them. Having a clear list of known issues makes the fix dramatically faster and cheaper.
Should I do this evaluation for every change?
Not the full checklist. For routine changes, focus on the security check and the test check. Run the full evaluation periodically and before major launches.