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AI can help teams build scalable software faster, but it does not supply the architecture, conventions, or verification that make software maintainable. Treat AI-generated code—and AI-proposed fixes—as proposals. Give the model clear requirements and project context, keep changes bounded, and require the same review, testing, security checks, and approval gates as other changes.
Start with the engineering system, not the code generator
AI assistance amplifies the conditions around it. DORA’s 2025 report describes AI as magnifying an organization’s existing strengths and weaknesses: teams with clear requirements and effective feedback can use it to accelerate work, while teams with weak conventions or limited verification risk producing confusion and defects more quickly.
That makes the first scaling decision organizational. Agree on how work is specified, how architectural decisions are recorded, what checks must pass, and who can approve changes. A faster way to produce code is useful only when the team can still understand, verify, operate, and change what it produces.
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Define the boundaries AI must work within
Before asking for an implementation, make the constraints that are easy to miss explicit. Identify the relevant interfaces, data ownership, error behavior, security requirements, performance targets, and local coding conventions. Include the intended outcome and the conditions that would make a proposed solution unacceptable.
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Choose system boundaries to fit requirements, team ownership, and operational constraints. No single architecture style is universally scalable. A model can help explore options, but it cannot decide what is appropriate without accurate context about the system and its trade-offs.
Write a bounded task
Describe one change at a time, including what should change, what must remain unchanged, and how the result will be verified. Ask for a plan or a small implementation rather than a broad rewrite. Small diffs are easier to compare with the intended outcome and safer to review, test, and revert.
Provide trusted project context
Supply the relevant conventions, interfaces, and architectural decisions through the team’s approved workflow. Do not assume a model knows the repository’s unwritten rules or that plausible-looking APIs exist in the project. Check that the result respects those constraints rather than relying on its explanation.
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- Define the task and constraints. State the expected behavior, affected boundaries, security and performance considerations, and acceptance checks.
- Request a plan or bounded change. Ask for the smallest useful implementation, and require assumptions or uncertainty to be made visible.
- Inspect the diff against the project. Verify that it fits the architecture and purpose, follows local conventions, handles errors appropriately, and does not silently expand scope.
- Run the existing verification pipeline. Use the project’s tests and analysis checks; add coverage for changed behavior and relevant edge cases.
- Examine dependencies and interfaces. Review newly introduced libraries, packages, services, and API usage. Confirm that they are real, acceptable, maintained according to team policy, and needed.
- Get domain-aware human review. A reviewer familiar with the affected system should assess correctness, security, readability, and maintainability—not only whether the code compiles.
- Require normal approval before merging or acting. Do not treat a model’s confidence as an approval or bypass established review and release controls.
- Capture recurring lessons. If reviews repeatedly uncover the same missing constraint or convention, update project guidance, acceptance criteria, or tests so future work has better context.
NIST’s DevSecOps reference model says AI-generated outputs should be reviewed through established processes, including peer review, security validation, automated testing, and approval workflows. The same principle applies to AI-proposed corrections: a suggested fix is not authorization to change software or system state.
Choose verification checks by risk
There is no single test that establishes that generated code is safe and maintainable. NISTIR 8397 describes a broad set of software verification techniques, including threat modeling, automated testing, static code scanning, checks for hardcoded secrets, built-in protections, black-box and structural tests, historical tests, fuzzing, web application scanning where applicable, and checks of included libraries, packages, and services.
Use that as a menu, not a requirement to run every technique on every pull request. Match checks to the change’s exposure, data sensitivity, system boundaries, and potential impact. A change to an internal formatting helper and a change to authentication or sensitive data handling do not present the same risk.
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- Behavior: Run automated tests and add cases for changed behavior, relevant edge cases, and regressions.
- Security: Consider threat modeling, secret checks, static scanning, and application scanning when the affected code or exposure warrants them.
- Robustness: Use structural tests, historical cases, or fuzzing where unusual inputs and failure modes matter.
- Supply chain: Inspect libraries, packages, and services introduced or changed by the implementation.
- Operations: Check the relevant performance, error, and operational expectations before accepting a change.
Review maintainability, not just correctness
Code can pass tests and still make a system harder to change. GitHub’s guidance for reviewing AI-generated code recommends checking whether it fits the project’s purpose and architecture, whether it is readable and maintainable, and whether dependencies are appropriate. It also calls attention to hallucinated APIs and ignored constraints.
During review, ask whether a future maintainer can understand why the change exists, whether names and structure make responsibilities clear, and whether the implementation adds unnecessary abstraction or duplication. Check that behavior and error handling are consistent with neighboring code. When a change is complex or sensitive, collaborative review by people with relevant domain knowledge is especially important.
Do not accept an explanation from the model as evidence that the code is correct. Compare the actual diff with the requirement, inspect how it interacts with surrounding components, and verify behavior through the project’s checks.
Use AI productivity figures as signals, not promises
DORA reported that 75% of its 2024 survey respondents outside Google said generative AI had a positive productivity impact. In the same reported findings, 39% of developers outside Google said they trusted AI output quality only “a little” or “not at all.” These are survey responses, not proof of a guaranteed productivity gain or a universal level of distrust for any particular team.
DORA’s 2025 report summarizes more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals worldwide. That figure describes the scale of the report’s research summary; it does not mean every participant answered every outcome question. The practical lesson is to evaluate AI in your own workflow: whether it helps deliver useful changes without increasing defects, review burden, or maintenance cost.
Make adoption a team and workflow change
DORA recommends clear acceptable-use policies, rigorous review and testing, opportunities to learn, voluntary use, and open discussion of how developers use time saved by AI. Make expectations concrete: which data may be shared with approved tools, which uses require extra review, and which approval or release controls remain mandatory.
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Give developers room to learn, but do not make adoption itself a performance target. Discuss whether time saved is going to better testing, design, documentation, or other valuable work. Use operational feedback—logs, vulnerabilities, performance metrics, and incidents—to inform planning and development. NIST describes AI assistance in analyzing this information and recommending remediations, while requiring generated corrective actions to go through established review and approval before software or system state changes.
Decide whether the workflow is actually scaling
Track the effect on the whole delivery system rather than counting generated lines or accepted suggestions. Review whether changes still fit architectural boundaries, whether required checks and approvals are happening, and whether operational feedback is reaching the people who plan and implement work. Look for recurring review findings, defects, security issues, and maintenance friction that indicate the team needs better constraints or verification.
If implementation volume rises while review quality or operational understanding falls, slow the scope of AI-assisted work until the team restores those controls. The goal is not maximal code generation. It is more useful change that the organization can continue to understand and safely evolve.
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