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AI-generated code creates less rework when you treat generation as a controlled engineering loop rather than a one-shot request. Give the model a precise outcome, repository context, constraints, examples, acceptance criteria, and edge cases; ask for a plan and tests before asking for a patch. Then review in stages: run objective checks, verify intent and architecture, inspect maintainability and dependencies, look for AI-specific failures, and obtain qualified human approval before production. This approach cannot guarantee a fixed percentage reduction—no authoritative source has established one—but it makes misunderstandings visible earlier and gives your team measurable signals for improvement.
Why does AI-generated code need rework?
Most rework starts before the model writes code. A vague request leaves important decisions implicit: which interface is authoritative, what behavior must remain compatible, which files are in scope, how errors should be handled, and what “done” means. A model can produce syntactically polished code while solving a slightly different problem.
Rework also comes from a mismatch between generated code and the surrounding system. The patch may use an API that is not installed, violate a repository convention, add an unnecessary dependency, weaken a security control, or pass a narrow test while failing a real edge case. Generated tests can share the same mistaken assumption as generated implementation code, so passing tests alone is not proof of correctness.
The practical answer is a two-part loop:
- Prevent misunderstandings in the prompt: supply context, boundaries, examples, and an explicit acceptance contract.
- Detect remaining errors in review: combine automation with human checks for intent, architecture, risk, and ownership.
Use the same loop for a small refactor and a large feature, but increase the depth and expertise of review as the impact rises.
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What should I put in a prompt so the first draft is usable?
A usable prompt defines the outcome, gives the model the project facts it cannot infer safely, limits its freedom where mistakes are costly, and specifies how the answer must be returned. The following components provide a repeatable structure.
| Prompt component | What to provide | Why it reduces rework |
|---|---|---|
| Outcome | The user or system result, plus a testable definition of “done.” | Prevents a technically valid patch that does not solve the business problem. |
| Context | Relevant README sections, interfaces, contracts, recent changes, existing patterns, and trusted documentation. | Grounds decisions in the repository instead of generic examples or guessed APIs. |
| Boundaries | Language and runtime versions, API compatibility, security rules, performance limits, data-handling restrictions, and files that must not change. | Stops the model from taking shortcuts that create later rewrites or compliance issues. |
| Examples | Representative inputs and expected outputs, including at least one failure or boundary case. | Shows behavior that prose often leaves ambiguous. |
| Plan request | Ask for assumptions, a short implementation plan, risks, and a test plan before the patch. | Exposes misunderstandings while they are cheap to correct. |
| Output contract | A fixed response shape such as summary, files changed, patch, tests, results, and unresolved questions. | Makes omissions easy to spot and responses easier to review or automate. |
State the outcome in observable terms
“Add idempotency” is incomplete. Say which endpoint is affected, what repeated requests return, how long an idempotency key remains valid, and what happens when the same key is reused with a different payload. Tie the requirement to observable checks, such as response codes, persistence behavior, and concurrency handling.
Give the model authoritative context
Attach or quote the relevant contract rather than asking the model to infer it. Identify which source wins if documents conflict—for example, the current API schema over an old README. Include existing middleware, naming patterns, error types, and a nearby implementation that the project already accepts. Context should be relevant and bounded; dumping an entire repository can make important constraints harder to notice.
Make constraints explicit
Name versions and non-negotiable limits: “Use the project’s current runtime,” “preserve public response codes,” “do not add dependencies,” “never log payment data,” or “do not modify migration history.” Include security, privacy, latency, memory, licensing, and deployment constraints when they apply. Tell the model which files are read-only and which are allowed to change.
Include examples and a failure example
Examples should cover ordinary and awkward cases: empty input, duplicate requests, timeouts, malformed data, authorization failures, and large values where relevant. A negative example is especially useful because it defines behavior the model must not accidentally implement.
Should I ask for a plan before asking for code?
Yes. A plan-first exchange separates understanding from implementation. It gives you a small, reviewable artifact before the model commits to a design and lets you correct assumptions without untangling a large patch.
- Provide the goal and context. Include the contract, constraints, examples, and trusted sources.
- Request assumptions. Ask the model to list anything it cannot establish from the supplied material instead of silently guessing.
- Request a short implementation plan. Require affected files or modules, data-flow changes, compatibility considerations, and rollback implications.
- Request threat and edge cases. Include authorization, malformed input, retries, concurrency, partial failure, and data exposure where applicable.
- Request a test plan. Separate unit, integration, contract, regression, performance, and security checks as appropriate.
- Review the plan yourself. Correct scope or architecture errors before requesting a patch.
- Request the smallest patch that satisfies the approved plan. Ask the model to identify any deviation and explain why it is necessary.
This workflow follows current guidance from OpenAI on defining roles and workflows, using structured tool interactions, testing, and iterative validation. Microsoft guidance likewise emphasizes clear instructions, decomposition, output structure, and grounded context, while noting that prompting techniques vary by model and that some techniques are unsuitable for reasoning-oriented models.
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A prompt template you can adapt
You are modifying the billing service.
Goal:
Add idempotency for POST /charges. A repeated request with the same key and
identical payload must return the original result without charging twice.
Trusted context:
- The attached API contract is authoritative for request and response shapes.
- Existing idempotency middleware shows the project’s storage and expiry pattern.
- Follow the repository’s current error types and logging conventions.
Constraints:
- Preserve public response codes.
- Do not add dependencies.
- Never log payment data or full request bodies.
- Keep changes within the billing service and its tests.
Examples:
- First request: creates one charge and returns the normal success response.
- Retried request with the same key and payload: returns the stored result.
- Same key with a different payload: returns the documented conflict response.
- Missing or malformed key: follows the existing validation behavior.
First return:
1. Assumptions and unanswered questions.
2. A five-step implementation plan.
3. Threat and edge cases.
4. A test plan with expected assertions.
After the plan is approved, provide the smallest patch. End with:
- Files changed
- Commands to run
- Test results
- Unresolved risks
How should I review AI-generated code?
Review in layers, starting with checks that are cheap and objective, then moving to questions that require system knowledge and judgment. Do not merge because the code looks professional or because a generated test suite passes.
1. Run objective checks first
Compile or build the change, run unit and integration tests, lint, type-check, and execute the project’s dependency and security scans. Investigate every warning and error rather than treating a green command as a complete review. GitHub’s review guidance specifically calls for confirming that code compiles, tests pass, and warnings or errors are addressed; it cites CodeQL, Dependabot, and code-quality checks as examples of useful automation.
Run the checks against the full affected surface, not only a new test file. A changed interface can break consumers that the model did not inspect. If a check cannot run locally, record that fact and run it in the appropriate CI environment before approval.
2. Verify context and intent
Compare the patch with the ticket, architecture documentation, README, API contracts, and established patterns. Ask:
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- Does it preserve compatibility, authorization, data ownership, and error semantics?
- Are assumptions about business rules or user behavior documented and correct?
- Is the scope limited to what was requested, or did the model introduce unrelated cleanup?
This is where a human who understands the product and architecture adds value that a compiler cannot.
3. Inspect maintainability and dependencies
Check naming, control flow, modularity, comments, documentation, and test readability. Generated code often appears polished while hiding duplicated logic, unnecessary abstraction, brittle mocks, or a function that is harder to maintain than the original code.
For every new dependency, confirm that it exists, is maintained, has an acceptable license, fits the supported runtime, and is actually needed. Verify package names and APIs against the project’s lockfile or official documentation instead of trusting a plausible-looking import.
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4. Hunt for AI-specific failure modes
Search deliberately for errors that are common in generated output:
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- Constraints mentioned in the prompt but ignored in implementation.
- Incorrect edge-case logic around retries, time zones, null values, concurrency, or partial failure.
- Tests that were skipped, weakened, deleted, or written to mirror the implementation rather than the requirement.
- Insecure defaults, over-broad permissions, secrets in logs, unsafe deserialization, or missing validation.
- Code that compiles and passes narrow tests but does not match the intended architecture or data model.
Read the diff and the tests together. A test that asserts only that a function returns a value may miss duplicate side effects, authorization failures, or incorrect persistence.
5. Obtain qualified human approval for high-risk changes
Security, authentication, authorization, payment or other money movement, privacy-sensitive data, migrations, and production infrastructure deserve review by someone qualified in that domain. The UK Home Office Engineering Guidance and Standards states: “AI-assisted outputs MUST be reviewed and approved by a human before reaching production.” It also states that AI tools cannot replace human judgment, understanding, ownership, or responsibility for system decisions and changes.
For a routine, low-impact change, one appropriate reviewer may be enough. For a high-impact change, require the approvers and testing evidence defined by your organization’s existing change policy rather than inventing an AI-only exception.
6. Keep the work traceable
Mark AI-assisted commits or pull requests when policy permits, preserve the prompt or design context needed to understand the decision, and record commands, test results, review comments, and final approvals. Traceability makes later incident analysis possible and helps teams identify which kinds of generated work repeatedly need correction.
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Use build-time controls for rules that should not depend on reviewer memory. OWASP SAMM recommends explicit governance and controls because AI can generate code, tests, configuration, and infrastructure at a volume and speed that existing processes were not designed to handle. Keep secrets and signing keys away from AI tools, enforce static-analysis and architectural rules in the build, verify dependencies, and test misuse cases. Automation should block known violations; humans should decide intent, risk, and acceptable trade-offs.
How much review is appropriate for different changes?
Review depth should track potential impact, not how quickly the model produced the patch. Use a risk-based policy like this and adapt it to your organization’s controls.
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| Change profile | Minimum checks | Additional assurance |
|---|---|---|
| Low impact Documentation, isolated refactor, non-sensitive UI copy |
Build or lint checks, focused tests, intent and scope review | One knowledgeable reviewer; confirm no unexpected files or dependencies changed. |
| Moderate impact Business logic, public API behavior, shared library |
Unit and integration tests, type and static analysis, dependency review, architecture check | Peer review of edge cases and compatibility; record assumptions and unresolved risks. |
| High impact Authentication, authorization, privacy, money movement, migrations, production infrastructure |
All applicable automated checks, security testing, failure and rollback tests, operational validation | Qualified human approval before production, explicit threat review, traceability, and a tested rollback or recovery path. |
A small diff can still be high risk: changing one authorization condition or migration statement may have a larger blast radius than adding several lines of display logic.
How can I measure whether rework is actually decreasing?
There is no authoritative universal percentage reduction tied to a particular prompt format. Measure your own workflow over comparable periods and change types instead of treating an industry number as a promise.
| Signal | What it indicates | How to interpret it |
|---|---|---|
| Failed or missing tests | Requirements or edge cases were not covered before review. | Track failures by category, not just count; a single severe regression matters more than several cosmetic failures. |
| Static-analysis and security findings | Objective quality or security issues escaped generation and initial review. | Separate new findings from baseline debt and record severity. |
| Review-comment count and correction cycles | How much reviewer effort a generated change consumes. | Normalize by change size and complexity; more comments can reflect healthier scrutiny. |
| Reopened defects | Issues discovered after apparent completion. | Connect each defect to the missed prompt detail or review control where possible. |
| Dependency corrections | Incorrect, unnecessary, vulnerable, or incompatible package choices. | Record whether verification happened before merge and whether policy blocked the issue. |
| Time from draft to approved merge | Total delivery effort, including review and rework. | Compare similar work; faster generation is not an improvement if approval time or incidents rise. |
Keep a baseline for non-AI-assisted work where possible. Also record the type of task, risk level, model, and reviewer experience so that a change in workload does not look like a prompting improvement.
Why does trust calibration matter when reviewing generated code?
Reviewers need to calibrate confidence to evidence, not to the fluency of the output. JetBrains Research and Lund University reported findings from 17 practitioners and 43 software professionals in a 2026 study, describing trust calibration as the central problem in reviewing AI-generated code.
The study’s concern is that generated lines can appear equally confident even when their risk differs. Review support should therefore expose risk and confidence at the granularity where developers focus—such as a changed function, data-flow path, dependency, or security-sensitive operation—rather than presenting an undifferentiated confidence signal.
In practice, treat a concise, familiar-looking patch as untrusted until it has evidence behind it. Give extra attention to code that touches authorization, persistence, external services, concurrency, parsing, cryptography, or irreversible side effects. A reviewer should be able to explain why the change is correct, not merely that it resembles existing code.
What prompt and review mistakes create the most avoidable rewrites?
| Mistake | Typical consequence | Correction |
|---|---|---|
| “Implement feature X” with no acceptance criteria | The model optimizes for a plausible implementation rather than the requested behavior. | Define observable success, failure behavior, and compatibility requirements. |
| Providing only a ticket summary | Repository conventions, contracts, and recent changes are ignored. | Supply the relevant interfaces, examples, trusted sources, and scope boundaries. |
| Asking for code and tests in one opaque step | Incorrect assumptions become embedded in both implementation and tests. | Request assumptions, plan, risks, and test design first. |
| Accepting a green narrow test run | Integration breaks, security issues, or contract violations reach review late. | Run the complete applicable build, integration, type, static, dependency, and security checks. |
| Reviewing style before behavior | Time is spent on formatting while a fundamental intent error remains. | Check objective correctness and requirements before maintainability polish. |
| Allowing unbounded “cleanup” | Diff size and risk expand, making failures harder to attribute. | Require the smallest patch and reject unrelated changes. |
| Letting the model choose dependencies silently | Packages may be nonexistent, unmaintained, incompatible, or unnecessary. | Require justification and verify each dependency against project and policy records. |
| Skipping human approval for sensitive work | Automation misses business, security, and operational trade-offs. | Require qualified approval before production and preserve the review trail. |
What does a complete AI-assisted change workflow look like?
Use this sequence for a feature, bug fix, or infrastructure change. Teams can automate the mechanical parts, but ownership remains with the engineers approving the result.
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- Classify the change. Identify affected users, data, systems, and the consequences of failure. Assign a risk level before generation.
- Assemble the context pack. Gather the authoritative contract, relevant files, constraints, examples, recent changes, and trusted documentation. Remove secrets, tokens, and unnecessary personal data.
- Write the prompt contract. State outcome, boundaries, acceptance criteria, edge cases, output format, and files that must not change.
- Ask for assumptions and a plan. Review the plan, threat cases, and test plan; correct misunderstandings before requesting implementation.
- Generate the smallest patch. Require a summary, files changed, tests, command output, and unresolved questions. Reject unexplained scope expansion.
- Run automated checks. Build, test, lint, type-check, scan dependencies, and run security or architectural rules that apply.
- Review behavior and design. Compare with the requirement, architecture, contracts, and failure cases. Inspect the diff and tests for omissions, hallucinated APIs, insecure defaults, and unnecessary dependencies.
- Obtain the required approval. Match reviewer expertise and number to the risk classification; do not bypass existing production controls.
- Record evidence and learn. Preserve the decision trail, then classify defects, review comments, and delays so future prompts and guardrails can target recurring causes.
The loop is complete only when the approved change is traceable and the team can explain both why it works and which checks support that conclusion.
How should teams improve the process over time?
Start with a small set of mandatory prompt fields and review gates, then refine them using your local measurements. If reviewers repeatedly find missing authorization checks, add an authorization section and a corresponding automated test to the prompt template. If dependency errors recur, make dependency verification a required output and a CI gate. If plans are accurate but patches are too broad, tighten the smallest-patch rule and scope checks.
Review the workflow itself periodically. GitHub’s guidance recommends collaborative review, automation, and continued improvement rather than treating review as a one-time sign-off. The goal is not to eliminate human review; it is to spend human attention where automation cannot establish intent, architecture, ownership, or acceptable risk.
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Frequently Asked Questions
Can AI-generated code be merged without a human review?
For production changes, the UK Home Office Engineering Guidance and Standards requires human review and approval. Even when a project permits automated merging for low-risk changes, the team should first define objective gates, scope limits, and an escalation path for exceptions.
Should I include the entire repository in an AI prompt?
Usually not. Provide the smallest authoritative context that covers the interfaces, conventions, constraints, and examples relevant to the change, and identify which source takes precedence when documents disagree. Excess context can hide the requirements that matter most.
How do I keep secrets safe while using an AI coding tool?
Do not place credentials, signing keys, payment data, or unnecessary personal information in prompts or tool context. Use redacted fixtures and approved access controls, and verify your organization’s retention and training policy before sending proprietary code.
What if the model’s plan conflicts with the existing architecture?
Stop before requesting the patch and point to the authoritative design or pattern it must follow. Ask the model to revise its assumptions and explain any remaining trade-off; if the architecture itself needs to change, handle that as a separately approved design decision.
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