Building a code review process around AI assisted coding means establishing clear ownership, updated review criteria, and structured workflows that treat AI output as a starting point rather than a finished product. Every line of AI-generated code should pass through human review before it reaches production, with reviewers trained to spot the specific failure patterns that AI tools introduce. The sections below break down the key questions teams are asking as they adapt their engineering practices in 2026.
What makes AI-generated code different to review?
AI-generated code is different to review because it can appear syntactically correct and logically plausible while still being functionally wrong, insecure, or poorly suited to the codebase it is entering. Unlike code written by a developer who understands the broader system, AI output is generated from patterns in training data, not from genuine comprehension of your architecture, business rules, or non-functional requirements.
This creates a specific challenge: reviewers cannot rely on the usual assumption that the author understood the intent behind the code. AI tools do not know what they do not know. They will confidently generate code that handles the happy path beautifully while silently ignoring edge cases, deprecated dependencies, or security constraints specific to your environment. Reviewers need to shift from checking whether code looks right to verifying whether it actually does the right thing in context.
What are the biggest risks in AI-generated code?
The biggest risks in AI-generated code are security vulnerabilities, hallucinated dependencies, subtle logic errors, and licence compliance issues. These risks are especially dangerous because they are often invisible at first glance and can pass through superficial reviews undetected.
- Security vulnerabilities: AI models are trained on public code repositories that contain known vulnerabilities. They can reproduce insecure patterns such as SQL injection risks, improper input validation, or hardcoded credentials without flagging them.
- Hallucinated dependencies: AI tools sometimes reference libraries, functions, or APIs that do not exist, or that exist but behave differently from what the generated code assumes.
- Logic errors in edge cases: AI-generated solutions often work for the main scenario but fail silently when inputs fall outside the expected range or when state is shared across components.
- Licence and IP risk: Code generated from training data may closely reproduce open-source code with restrictive licences, creating legal exposure if it enters a commercial codebase without review.
How should code review criteria change for AI output?
Code review criteria for AI output should expand beyond style and logic to include intent verification, security scanning, dependency auditing, and contextual fit. Standard review checklists assume the author had intent; with AI-generated code, the reviewer must reconstruct and validate that intent independently.
In practice, this means adding explicit checkpoints that would be redundant for human-authored code. Reviewers should ask whether the generated code actually solves the stated problem, not just whether it compiles and passes tests. They should verify that every external dependency referenced is real, maintained, and compatible with the project’s licence requirements. Security review should be treated as mandatory rather than optional, particularly for any code that handles authentication, data storage, or external API calls.
Teams should also evaluate whether AI-generated code fits the existing architecture and naming conventions, since AI tools tend to produce generic solutions that may conflict with established patterns in a mature codebase. Consistency with the surrounding system is a meaningful quality signal, not just an aesthetic preference.
What tools help teams review AI-generated code effectively?
The most effective tools for reviewing AI-generated code combine static analysis, security scanning, dependency checking, and AI-aware linting. No single tool covers every risk, so teams typically layer several together within their existing CI/CD pipeline.
- Static analysis tools such as SonarQube or Semgrep catch common code quality issues and can be configured with custom rules that reflect your organisation’s standards.
- Security scanners such as Snyk or Checkmarx identify known vulnerability patterns, including many that AI models are prone to reproducing.
- Dependency auditors verify that referenced packages exist, are actively maintained, and carry acceptable licences before code reaches production.
- AI-aware review assistants such as GitHub Copilot’s code review features or CodeRabbit can provide a secondary layer of automated commentary, though these should supplement human review rather than replace it.
The key principle is that tooling catches the systematic and the obvious; human reviewers handle context, intent, and architectural judgement. Investing in tooling reduces the cognitive load on reviewers so they can focus on the decisions that genuinely require human expertise.
Who should be responsible for approving AI-generated code?
Responsibility for approving AI-generated code should sit with a senior developer or tech lead who understands both the codebase and the business domain the code is serving. The developer who prompted the AI tool should not be the sole approver, for the same reason that authors do not proofread their own work.
Many teams are formalising this by updating their contribution guidelines to require at least one approval from a reviewer who did not generate the code, regardless of the author’s seniority. In higher-risk domains such as financial services, healthcare, or infrastructure, some organisations are requiring a second approval from a security-focused reviewer for any AI-assisted contribution that touches sensitive systems.
Ownership also matters after merge. The developer who accepts and merges AI-generated code should be treated as its owner going forward, fully accountable for its behaviour in production. Establishing this norm early prevents situations where bugs in AI-generated code go unowned because no individual developer feels responsible for code they did not write themselves.
How do you build a repeatable review workflow for AI code?
A repeatable review workflow for AI-generated code combines a documented checklist, automated tooling gates, clear ownership rules, and a regular retrospective loop to improve the process over time. The goal is to make thorough review the path of least resistance, not an extra burden on top of normal work.
Start by defining what must be true before any AI-generated code can be submitted for review: tests written, static analysis passed, dependencies verified, and the prompt or generation context documented alongside the code. This last point is underused but valuable. Knowing what the developer asked the AI to do makes it much easier for a reviewer to judge whether the output actually delivers on that intent.
From there, build your review checklist around the specific risk categories covered earlier: security, logic, dependencies, and architectural fit. Gate your CI pipeline so that automated checks must pass before a human reviewer is even notified. This preserves reviewer attention for decisions that require judgement rather than mechanical checking.
Finally, run a brief retrospective every quarter to review incidents or near-misses that originated in AI-generated code. These retrospectives are where the workflow improves. Patterns that emerge from real failures should feed directly back into your checklist, your tooling configuration, and your team training.
How Bloom Group Helps With AI Assisted Coding
We understand that adopting AI assisted coding at scale is not just a tooling decision; it is an organisational and engineering challenge that requires the right expertise at every layer. At Bloom Group, we work with mid-sized and large enterprises to design and implement development practices that make AI-generated code safe, consistent, and genuinely productive.
- We help teams define and document AI-specific code review criteria tailored to their codebase, risk profile, and regulatory environment.
- We integrate automated security scanning, dependency auditing, and static analysis into existing CI/CD pipelines so that review gates are enforced consistently.
- We advise on governance models for AI-generated code, including ownership rules, approval hierarchies, and audit trails that satisfy compliance requirements in regulated industries.
- We support Greenfield projects and scale-ups in building these practices from the ground up, so that good habits are embedded from day one rather than retrofitted later.
- Our team, 100% academically trained in Computer Science, AI, Mathematics, or related disciplines, brings the technical depth to evaluate AI output critically and build review processes that hold up under real production pressure.
If your team is scaling up AI assisted coding and wants to build a review process that actually works, we would be glad to help. Get in touch with us to discuss how we can support your engineering organisation.
Frequently Asked Questions
How do we train developers to review AI-generated code if they are not familiar with its failure patterns?
Start with structured sessions that walk developers through real examples of AI-generated code failures — hallucinated dependencies, insecure patterns, and edge case gaps — using samples from public post-mortems or sanitised internal incidents. Pair newer reviewers with senior engineers on the first few AI-assisted pull requests so that pattern recognition is built through practice rather than theory alone. Over time, your quarterly retrospectives (as described above) become one of the most effective ongoing training tools, since they ground learning in failures your own team has actually encountered.
Should we label or tag AI-generated code differently in our version control system?
Yes — tagging or labelling AI-assisted contributions in your version control system is a best practice that pays dividends during incident investigation, audits, and onboarding. A simple convention such as a commit tag, PR label, or metadata field indicating that code was AI-assisted (along with the tool used) makes it far easier to trace the origin of a bug or compliance issue after the fact. In regulated industries, this kind of audit trail is increasingly expected by compliance frameworks, so building the habit early avoids a painful retrofit later.
What is the biggest mistake teams make when first introducing AI code review processes?
The most common mistake is treating AI-generated code review as identical to standard code review, just with a few extra checklist items bolted on. This underestimates how fundamentally different the risk profile is: AI output can be confidently wrong in ways that human-authored code rarely is, and reviewers who are not explicitly primed for this tend to approve code that looks plausible rather than verifying that it is correct. The fix is to update your review culture — not just your checklist — so that scepticism toward AI output is normalised and expected rather than seen as slowing things down.
How do we handle AI-generated code in legacy codebases where documentation and test coverage are already poor?
In legacy environments, the risks of AI-generated code are amplified because the AI has no reliable context about undocumented business rules, implicit dependencies, or technical debt that shapes how the system actually behaves. Before introducing AI-assisted coding into a legacy codebase, it is worth establishing a minimum baseline: at least smoke-test coverage around the areas being modified and documented architectural boundaries that reviewers can reference. Treat AI contributions to legacy systems as higher-risk by default and apply stricter review criteria, including mandatory senior approval, until confidence in the process is established.
Can AI tools be used to help review AI-generated code, or does that create a conflict of interest?
AI-assisted review tools such as CodeRabbit or GitHub Copilot’s review features can add genuine value as a first-pass filter, catching obvious issues and surfacing potential concerns before a human reviewer sees the code. However, using AI to review AI output should be treated as a supplement, not a substitute, for human judgement — the same underlying limitations that produce errors in generation can also produce blind spots in automated review. The most effective approach is to use AI review tooling to reduce the mechanical burden on human reviewers, freeing their attention for context-dependent decisions about security, architecture, and business logic fit.
How should our review process differ for AI-generated tests versus AI-generated production code?
AI-generated tests carry their own distinct risks: they may test the happy path thoroughly while missing the edge cases that matter most, or worse, they may be written to pass the AI-generated implementation rather than to validate the intended behaviour. Reviewers of AI-generated tests should independently verify that the test cases cover the scenarios that could realistically fail in production, not just the scenarios the AI assumed were relevant. A useful heuristic is to ask whether the tests would catch the kinds of subtle logic errors described in this post — if they would not, they are providing false confidence rather than genuine safety.
At what point does it make sense to reject AI-generated code entirely rather than revising it through review?
Rejection is the right call when the AI output is so far from the intended solution that revising it would take longer than writing the code from scratch, or when it introduces risks — such as a fundamentally flawed security architecture — that cannot be patched at the review stage. A practical rule of thumb: if a reviewer finds themselves rewriting more than roughly half of a generated block to make it correct and safe, the value of the AI contribution has already been lost and starting fresh is the more efficient path. Establishing this as an explicit and accepted outcome in your team culture removes the implicit pressure to approve AI output just because it was generated.