The biggest risk with AI-assisted development is not that AI writes bad code. It is that AI writes plausible code: code that looks right, passes a quick glance and fails in a case nobody checked. AI tools can also produce a confident answer that is simply wrong, a problem known as Hallucination. Quality in an AI-assisted team depends entirely on the checks around the AI.
This page describes those checks. They apply to every project, whatever its size.
1. Clear specifications before any code
Quality starts with knowing what "correct" means. Before a feature is built, it has written acceptance criteria: what it must do, what it must not do and the edge cases that matter. AI can draft these from our notes, but a person agrees them with you. Without this step, nobody can say whether the code is right, however it was written.
2. Engineer review of every change
Every change is reviewed by an engineer before it is merged. The reviewer reads the code, not just the summary. They check that it does what the specification says, that it follows the architecture, that it handles errors and unusual inputs, and that it does not introduce security problems. For AI-drafted code, reviewers pay particular attention to invented functions or libraries, outdated patterns and logic that works for the obvious case only.
Reviews are not rubber stamps. Changes are sent back, rewritten or discarded when they are not good enough. The engineer who approves a change is responsible for it.
3. Automated tests on every change
AI makes it cheap to write tests, so we write more of them. Unit tests check individual functions. Integration tests check that parts work together, including databases and outside services. End-to-end tests click through key user flows in a real browser. All of them run automatically on every change, and a change that breaks a test does not get merged.
Tests are reviewed too. A test that always passes proves nothing, so reviewers check that tests would fail if the code were wrong.
4. Security checks
Automated scans look for known vulnerabilities in dependencies, secrets accidentally committed and common coding mistakes. Engineers review anything that touches authentication, permissions, payments or personal data with extra care. For sensitive projects we add a focused security review before launch. Our AI development security checklist lists what we check.
5. Design and product review
Engineers check what users do not see. Designers and product owners check what they do: that screens match the approved design, that copy is accurate and clear, that flows make sense and that the product works with a keyboard, a screen reader and a small phone.
6. Staged releases and your sign-off
New work goes to a test environment first, where you and your team can try it. Nothing goes live without your approval. For products with real users, we release gradually where possible, watch errors and performance closely, and can roll back quickly if something goes wrong. This step is where the Deployment process earns its keep.
7. Keeping quality high over time
Speed can create technical debt if nobody watches for it. We keep a short list of known shortcuts and their cost, fix them on a schedule rather than letting them pile up, and keep documentation current as the code changes. That keeps the product easy to change after launch, whether we continue or your own team takes over.
Who is accountable
AI tools are not accountable for anything. People are. Every project has a named lead responsible for quality, and every change has a named reviewer. If something goes wrong, you talk to a person who knows the code and will fix it.
Read more about how we use AI, our process, and our detailed guide on how we review AI-generated code.