AI-Assisted Development: What Teams Must Review Before Shipping
Use AI coding tools responsibly with a review checklist covering correctness, security, dependencies, tests, and ownership before software ships.
By RZoraTech Team, Engineering & Product · · 3 min read
AI-assisted development can accelerate drafting, exploration, and repetitive changes. The team still owns the code that ships. A useful review process checks whether a change meets the requirement, fits the architecture, protects data, and behaves correctly under failure. Accepting a plausible diff is not the same as validating a product change.
Begin with a reviewable requirement
Describe the expected behaviour and constraints before asking for implementation. Include who may perform the action, what happens when inputs are invalid, and how success will be verified. Keep changes small enough that a reviewer can trace their effect.
Provide relevant repository conventions and current documentation. A generated implementation may use an outdated API or assume a library that the project does not have. Verify dependencies, version compatibility, and migration guidance against the actual project. The same standard applies whether a human or a tool wrote the first draft.
Review the risky boundaries
Pay special attention to authentication, authorisation, payment logic, database queries, file uploads, and external calls. Check whether errors reveal sensitive information, whether retries duplicate writes, and whether logs collect more data than needed. Confirm that configuration values are not hard-coded secrets.
Inspect newly introduced dependencies rather than accepting them automatically. Understand the package purpose, maintenance status, and licence. Avoid a large dependency for a small helper when existing code already solves the problem. Generated comments should explain actual decisions, not merely assert that a function is safe.
Test outcomes and preserve ownership
Tests should exercise the user-visible behaviour and important failure paths. A test that repeats the implementation can pass while both contain the same mistake. For an access-control change, use different users and resources; for a payment change, simulate retries and partial failures.
Run the project's relevant checks and inspect the interface where layout matters. Assign a human owner who can explain the final design and maintain it. Track delivery and quality together rather than counting generated lines. AI assistance is most valuable when it reduces repetitive effort without weakening engineering accountability.
What to follow before development
- Write acceptance criteria and identify sensitive boundaries.
- Check generated APIs against installed versions.
- Inspect dependency, migration, and permission changes.
- Verify failure paths and document a rollback approach.
A practical example
An AI tool drafts a bulk customer export. The reviewer notices that it filters by a client-supplied organisation ID without checking membership. The team adds server-side authorisation, tests a second tenant, and verifies the exported fields. The important result is the protected workflow, not how quickly the initial endpoint was written.
Sources and further reading
Plan your next step
Build a team with the skills and ownership your roadmap needs through our dedicated engineering teams. For a related implementation guide, read release readiness.
Frequently asked questions
- Should AI-generated code receive a different quality standard?
- It should meet the same release standard as other code. Generated changes still need requirements, review, relevant tests, and an accountable maintainer.