Quick take: You don’t need to read code to judge its quality. Test coverage reports give you hard numbers on how much of your codebase is tested, revealing whether your development team is building reliably or cutting corners. Aim for 80% or higher on critical features.
Your AI vendor just delivered the first version. The demo looks good, but you have no idea if the code underneath will hold up when real users hit it. Most non-technical founders feel helpless here, trusting blindly or hiring expensive auditors.
You have more options than you think. While you can’t review code line-by-line, you can evaluate the signals that separate professional engineering from rushed hacks.
Overview of Evaluation Methods
| Method | Best For | Key Strength | Time Required |
|---|---|---|---|
| Test Coverage Reports | Critical features | Objective percentage metrics | 10 minutes |
| Documentation Quality | Onboarding new devs | Shows long-term thinking | 20 minutes |
| Code Analysis Tools | Security and standards | Automated, unbiased scanning | 15 minutes |
| Deployment Frequency | Team velocity | Reveals process maturity | 5 minutes |
| Error Monitoring Dashboards | Production stability | Real user impact data | 15 minutes |
| Dependency Health Check | Technical debt | Identifies security risks | 10 minutes |
1. Test Coverage Reports
Test coverage measures what percentage of your code is checked by automated tests. Professional teams maintain 70-90% coverage on business-critical features. Your vendor should be able to show you a coverage report in under 5 minutes.
Good coverage means when developers change something, tests catch breaks before users see them. Low coverage means every update is a gamble. Tools like Codecov or Coveralls generate visual reports showing exactly which parts of your codebase are tested and which are not.
Ask to see the coverage report for your most important features. If your authentication system is at 45% coverage while less critical features sit at 85%, that’s a red flag. Professional teams test the risky parts first.
The limitation is that coverage measures quantity, not quality. You can have 100% coverage with useless tests. Look for coverage combined with other signals.
2. Documentation Quality
Open the README file in your project repository. Can you understand what the project does, how to run it, and where to find things? If the documentation is sparse or outdated, the code probably is too.
Quality documentation signals that developers are thinking beyond this week’s sprint. It means they expect the code to live long enough that someone else will need to understand it. Check for architecture diagrams, setup instructions, API documentation, and explanations of major decisions.
This works for non-technical founders because documentation is written in plain language. You can judge clarity, completeness, and effort. If developers can’t explain their work clearly, that’s often a sign they don’t fully understand it themselves.
The catch is that documentation can be polished while code is messy. Use this as one signal among several, not a standalone verdict.
3. Code Analysis Tools
Static analysis tools scan code for security vulnerabilities, style violations, and common bugs. They don’t require you to read a single line. Tools like SonarQube, CodeClimate, or Snyk generate dashboards with letter grades and issue counts.
Ask your development team to run a scan and share the results. Look at the overall grade, the number of critical security issues, and trends over time. Is the grade improving or getting worse? Are security vulnerabilities being fixed or piling up?
These tools catch about 70% of common problems automatically. They flag things like hardcoded passwords, SQL injection risks, and outdated libraries with known exploits. A clean scan doesn’t guarantee perfect code, but a scan full of critical issues is a definite warning sign.
The limitation is false positives. Sometimes tools flag things that aren’t actually problems. Ask your team to explain any critical issues rather than demanding zero warnings.
4. Deployment Frequency
Teams that deploy small updates frequently tend to write better code than teams that deploy giant releases every few months. Frequent deployments force good practices like automated testing, modular architecture, and rollback procedures.
Check your deployment history in your hosting dashboard (AWS, Vercel, etc.). How often does code go to production? High-performing teams deploy daily or multiple times per week. Teams deploying monthly are probably batching changes, which makes debugging harder and increases risk.
This metric is accessible to non-technical founders because it’s just dates and frequency. No code reading required. If your team deployed 6 times in the last month with no major incidents, that’s a strong signal of mature processes.
The exception is very early-stage projects where frequent deployment doesn’t make sense yet. Use this metric once you’re past the first prototype phase.
5. Error Monitoring Dashboards
Tools like Sentry, Rollbar, or Datadog track errors that happen in production. They show you how many users are hitting bugs, which errors are most common, and whether error rates are increasing or decreasing.
You don’t need to understand the technical details of each error. Focus on the trend lines and response times. Are errors being fixed within hours or sitting unresolved for weeks? Is the error rate trending up or down? Are the same errors recurring?
This is one of the most valuable tools for non-technical founders because it shows real user impact. A codebase might score well on tests and scans but still crash frequently for users. Error monitoring reveals the truth.
The limitation is that you need users to generate errors. This doesn’t help you evaluate code quality before launch. Use it as an ongoing quality check once you have traffic.
6. Dependency Health Check
Your AI project likely uses dozens of open-source libraries. Each one is a potential security risk if it’s outdated or unmaintained. Tools like Snyk, Dependabot, or npm audit scan your dependencies and flag vulnerabilities.
Ask to see the dependency report. How many dependencies have known security issues? Are dependencies up to date or years old? Are you depending on abandoned projects that no longer receive updates?
A healthy project has mostly up-to-date dependencies with few or no critical security vulnerabilities. Seeing 15 high-severity vulnerabilities in your authentication libraries is a major red flag. These reports are generated automatically and easy to interpret.
The catch is that some vulnerabilities don’t apply to how you’re using the library. Not every warning is critical. Focus on the high and critical severity issues, and ask your team about their remediation plan.
How We Chose These Methods
We selected evaluation methods that are accessible to non-technical founders, provide objective data rather than subjective opinions, and can be checked in under 30 minutes. Each method has tools that generate visual dashboards or reports, eliminating the need to read code directly.
We prioritized methods that reveal both current code quality and team practices. A one-time audit tells you where you are today. Deployment frequency and error trends tell you whether quality is improving or degrading.
Frequently Asked Questions
What’s a good test coverage percentage for an AI project?
Aim for 70-80% overall, with 80-90% on critical business logic like authentication, payments, and data processing. AI model code itself is harder to test with traditional coverage metrics, but the infrastructure around it should be well-tested.
Can these methods replace a professional code audit?
No. These methods give you strong signals about code quality and help you spot red flags, but they don’t replace deep technical review. Use them for ongoing monitoring and to decide whether a full audit is warranted.
How often should I check these metrics?
Review test coverage and dependency health with each major release. Monitor error dashboards and deployment frequency weekly. This takes about 20 minutes per week once you have dashboards set up.
What if my development team refuses to share these reports?
That’s a red flag. Professional development teams expect clients to want visibility into quality metrics. If your team is defensive about sharing coverage reports or error dashboards, they may be hiding problems.
Should I set specific requirements in my contract?
Yes. Specify minimum test coverage percentages, maximum acceptable critical security vulnerabilities, and required documentation standards. Make these part of your acceptance criteria for deliverables.
Key Takeaways
- Test coverage reports provide objective metrics on how thoroughly code is tested, with 70-80% being a reasonable target for most projects
- Documentation quality signals whether developers are building for long-term maintainability or just shipping features
- Automated code analysis tools catch security vulnerabilities and common bugs without requiring you to read code
- Frequent deployments indicate mature engineering practices and reduce risk compared to big-bang releases
- Error monitoring shows real user impact and reveals whether bugs are being fixed quickly or ignored
- Dependency health checks identify security risks from outdated or vulnerable third-party libraries
- None of these methods alone is definitive, but together they give non-technical founders strong visibility into code quality
SFAI Labs helps non-technical founders evaluate and improve AI development quality. We provide code audits, team assessments, and ongoing quality monitoring. Schedule a 30-minute code quality review.
SFAI Labs