Requirement Alignment Audit
A requirement alignment audit is a repeatable review that checks whether implemented behavior still matches the current, approved product requirements before a change ships. It compares the product decision, the written requirement, the acceptance criteria and what the code actually does, and records each mismatch as a specific finding instead of a vague sense that something is off.
How a Requirement Alignment Audit Works
The term is used in Prodstack's writing on AI-built codebases rather than in a formal standard. It borrows from familiar practices, such as requirements reviews and code review, but focuses on one question: is every important behavior in the code authorized by a requirement that is still current?
The audit runs as a fixed checklist across six areas:
- Product: Is the intended behavior still current? Has the underlying decision changed since the requirement was written?
- Requirements: Is every major behavior stated explicitly? Are important states and edge cases defined rather than assumed?
- Decisions: Can important decisions be traced to a source? Are superseded decisions clearly marked so nobody builds against them?
- Implementation: Does current behavior match the requirement? Did the agent or developer add behavior nobody authorized?
- Verification: Can the acceptance criteria be checked against the implementation? Are deviations visible rather than buried?
- Maintenance: When a requirement changed, were dependent tickets and expectations updated to match?
The output is a short comparison of requirement, current implementation and status. Each row ends in one of three outcomes: aligned, drift to fix in code, or an outdated requirement to update. The audit depends on requirements-to-code traceability, because you need to know which code belongs to which requirement before you can compare them.
Why a Requirement Alignment Audit Matters
In AI-assisted development, behavior changes through many small generations, and each one usually looks reasonable on its own. The problem is the total. A fixed audit catches mismatches while they are still cheap to fix, before other features start depending on them.
It also changes the quality of the conversation. "The checkout flow feels off" is a hunch that gets forgotten. "The requirement says keep the cart after a declined payment, and the code clears it" is a finding with an owner. Running the same checklist each time means the result does not depend on who happens to remember which decision.
Requirement Alignment Audit Example
Before a release, a founder audits the checkout and account areas:
| Requirement | Current implementation | Status |
|---|---|---|
| Keep cart after a declined payment | Cart is cleared | Drift: fix code |
| Account recoverable for 30 days | Account is hard-deleted | Drift: fix code |
| Show order history for 12 months | Shows 24 months | Requirement outdated: team chose 24 last month |
Two items go back to the backlog as fixes. The third is a legitimate decision that never made it into the requirement, so the requirement is updated instead. This audit is part of a broader practice for preventing context drift in AI-generated codebases.
Requirement Alignment Audit vs. Code Review
Code review asks whether a change is correct, readable and safe. A requirement alignment audit asks whether the resulting behavior is authorized by current requirements. A change can pass code review and still fail the audit, because clean, well-tested code can do something the product never decided to do. The audit is how teams catch context drift that code review alone misses.