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Use an AI coder as a bounded, reviewable contributor—not as an autonomous owner of your codebase. Give it a small change, current project context, and observable acceptance criteria; ask for a plan when the work crosses modules; inspect every diff; run tests and security checks independently; and keep the merge decision with a human who understands the system. Plausible code, clean syntax, or an AI-generated review is not proof of correctness.
What “vibecoding” means in practice
“Vibecoding” is useful shorthand for building software by directing an AI assistant or coding agent through conversation, prompts, and iterative corrections. It is not a formal engineering method with one agreed definition. The practical question is how much responsibility you delegate while keeping behavior, security, and maintainability under control.
An assistant can explain a function, suggest an improvement, or draft unit tests. An agent can take a repository task, edit files, and run validation commands. Both can save time, but both can also produce code that looks convincing while misunderstanding the requirement, missing an architectural constraint, or introducing a security defect. GitHub’s guidance describes these limits for its own tools; the same supervision principle applies to any AI coding product.
The safe division of labor is straightforward: you define the problem and constraints, supply relevant context, evaluate the proposed design, and decide whether the result belongs in the codebase. The AI supplies drafts, alternatives, and mechanical help.
Choose the least-autonomous mode that fits the work
Match the tool’s autonomy to the size and risk of the change. A local completion is easy to inspect but has little system-wide awareness. Chat is useful for explanation and focused proposals. A repository agent can handle a larger issue, but requires explicit scope, instructions, and validation.
| Mode | Typical role | Questions to answer before using it |
|---|---|---|
| Inline completion | Offers code while you type, using nearby code and other local context. | Is the change local and easy to inspect immediately? Could local context hide an architectural or dependency problem? GitHub notes that inline suggestions have limited context and may not detect larger design issues. |
| Chat | Explains code, answers coding questions, proposes improvements, and helps draft tests. | Is the task mainly explanation or a focused proposal? Have you supplied the relevant files and conventions? How will you verify both the explanation and the code? GitHub documents these use cases and cautions that generated output can be inaccurate or incomplete. |
| Repository coding agent | Works on an issue across files, following repository instructions and validation steps. | Are acceptance criteria explicit? Can you review every changed file and command? Will tests and security checks run before merge? GitHub recommends clear tasks, project context, human review, and command review for agent work. |
Do not choose an agent merely because the task sounds tedious. A five-minute local edit that you can reason about is often safer than delegating it. Use broader autonomy when the work is still bounded, the expected behavior is testable, and you have time to inspect the result.
1. Select a small change with an observable outcome
Start with a task that can be reviewed as a unit. “Improve the billing system” is not a task; “reject an expired coupon at checkout, return the existing validation error, and add tests for expired and valid coupons” is. A good first assignment has one primary behavior, a known area of the repository, and a clear way to tell whether it succeeded.
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Good candidates
- Explain a function or unfamiliar module and identify its inputs, outputs, and failure paths.
- Propose a narrow refactor that preserves externally visible behavior.
- Draft tests for behavior you can state precisely.
- Implement a small bug fix with a reproducible failing case.
- Update a localized piece of documentation or a repetitive transformation.
Bad candidates for an unbounded first pass
- Reorganizing an entire architecture without an agreed design.
- Changing authentication, authorization, payment, or data-retention behavior without a security-focused review plan.
- Upgrading a major dependency and changing all callers in one autonomous run.
- Deleting or rewriting files when you cannot explain the expected final state.
For an agent, write the task like an issue. Include the problem, constraints, likely files or modules, acceptance criteria, and the commands that should validate the result. GitHub says well-scoped tasks and additional project instructions help an agent understand how to build, test, and validate its changes; that is guidance about its products, not a guarantee that an agent will infer your intent.
A task brief you can reuse
Goal: [one observable behavior to add or fix]Context: [relevant modules, interfaces, data constraints, and conventions]Acceptance criteria:- [expected behavior for the normal case]- [expected behavior for invalid, missing, or boundary input]- [compatibility or performance constraint]Validation: [specific project test, lint, type-check, build, or security commands]Boundaries: do not change [unrelated modules, public APIs, schemas, or generated files]Replace each bracketed item with project-specific text before sending the brief. The acceptance criteria are a contract for review, not a prompt to accept whatever output the agent produces.
2. Supply current, selective context
More context is not automatically better. Give the assistant the files, interfaces, tests, configuration, and conventions that determine the change. Remove stale design notes, unrelated modules, and contradictory examples. GitHub’s Copilot Chat guidance recommends selective context and keeping it current because irrelevant or outdated material can dilute or misdirect an answer.
Context worth including
- The implementation file and the callers that define its contract.
- Relevant types, schemas, API specifications, and error conventions.
- Nearby tests showing naming, fixtures, and expected edge-case behavior.
- Repository instructions covering formatting, supported runtime versions, generated files, and required checks.
- A short explanation of constraints that are not obvious from code, such as backward compatibility or data-privacy rules.
Context to leave out
- Old migration plans that no longer describe the deployed system.
- Large directories pasted merely because they are available.
- Secrets, credentials, customer data, or proprietary material that the tool does not need.
- Speculative requirements that have not been approved.
State what the assistant must not infer. For example, say that a public function’s signature cannot change, that a database migration must be backward-compatible during rollout, or that a validation error must retain its existing shape. These constraints are easier to check in a diff than to reconstruct after an agent has edited several modules.
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Inline suggestions are optimized for local context. A change that crosses modules, alters a data flow, or introduces a new abstraction deserves a written plan first. Ask the AI to identify the files it expects to touch, the invariants it will preserve, the edge cases it will test, and the commands it will run. Review that plan before allowing implementation.
This plan is a review aid, not a correctness certificate. GitHub warns that inline suggestions may miss broader architecture problems and that agents may have limited scope or fail to understand developer intent. Your job at the planning stage is to catch an incorrect boundary before it becomes a large diff.
Questions for a plan review
- Does the proposed entry point match the existing architecture, or is the AI creating a parallel path?
- Which public contracts, schemas, or serialized formats could change?
- What happens on missing, malformed, duplicated, delayed, or unauthorized input?
- Does the plan add an abstraction that removes duplication, or merely move complexity into another file?
- Can the stated tests distinguish the required behavior from an implementation that happens to pass?
- What is deliberately out of scope?
Reject or revise the plan if it expands the task without a reason. A short, explicit plan also gives you a baseline for comparing the eventual diff: if the implementation changes a different set of files or solves a different problem, stop and investigate.
4. Review the diff as the person accountable for the system
Never approve an AI change by reading only the final explanation. Inspect the actual diff and the surrounding code. Start with the list of changed files, then read each hunk in context. Confirm that every change supports an acceptance criterion and that no unrelated cleanup slipped in.
Behavior and design checks
- Trace the normal path from input to output and verify that it matches the requirement.
- Trace error, timeout, retry, cancellation, and partial-failure paths.
- Check boundary values, empty collections, nullability, concurrency, and ordering assumptions where they apply.
- Look for changed defaults, feature flags, permissions, logging, metrics, and externally visible error messages.
- Compare the implementation with existing architecture and style rather than judging it in isolation.
- Identify duplicated logic, unnecessary wrappers, speculative abstractions, and dependencies added without need.
- Check that tests assert behavior and meaningful failure cases, not just that the new lines execute.
GitHub’s responsible-use guidance says generated code should always be reviewed and tested for requirements, errors, and security concerns. It also advises ensuring that generated code fits the existing architecture and style. Treat that as a minimum review standard, not as an endorsement of any particular patch.
Useful local checks
- Run
git statusand confirm that the working tree contains only expected work. - Read the complete
git diff, including configuration, scripts, migrations, and generated artifacts. - Search for every changed symbol’s callers and tests; an apparently private change may have a wider contract.
- Compare the agent’s claimed validation with the commands’ actual output and exit status.
- Inspect the final diff again after any follow-up edits; fixes can introduce new defects.
Do not let a polished summary substitute for evidence. An AI can describe an implementation confidently even when it has not run the command it claims to have run or has misunderstood a test’s coverage.
5. Validate behavior independently
Run the project’s normal checks yourself or in a controlled CI environment. Use the repository’s documented commands for unit and integration tests, type checking, linting, builds, migrations, and packaging. The exact command depends on the project; what matters is that the check tests the requirement rather than merely reproducing the AI’s implementation.
Make tests prove the contract
- Include a test for the requested normal behavior.
- Include invalid and boundary inputs that could expose a plausible but wrong implementation.
- Verify observable outputs, state transitions, side effects, and error handling.
- For a bug fix, retain a regression test that failed before the change when practical.
- For a refactor, demonstrate unchanged behavior at the public boundary.
A passing test suite can still miss an untested requirement. Read the test assertions and coverage relevant to the change; do not assume that green status means the feature is correct.
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Generated code can be syntactically valid and semantically wrong, and it can contain vulnerabilities. Review security-sensitive changes with the same controls used for human-written code: threat modeling appropriate to the feature, secure-coding review, dependency checks, static analysis, secret scanning, and runtime or integration tests where your team requires them.
Security questions to ask
- Can untrusted input reach a query, command, template, file path, URL, or deserialization boundary without the project’s normal validation and encoding?
- Did the change broaden permissions, expose data in logs, weaken transport or cookie settings, or alter authorization checks?
- Are error messages and telemetry revealing secrets or personal data?
- Did a new dependency, script, or generated file introduce a supply-chain or update risk?
- Do retries, caching, and idempotency behave safely for sensitive operations?
GitHub’s security and code-quality documentation describes an evaluation harness containing over 2,300 alerts from public repositories with test coverage. That figure describes GitHub’s internal evaluation set, not a general AI-code success rate and not evidence that a suggestion is safe for your repository. Use your own security process and require a developer to verify any automated finding or fix.
7. Keep dangerous commands and file changes under human control
An agent should not have an unreviewed license to delete files, rewrite history, alter production data, or install arbitrary tooling. GitHub’s agent guidance specifically calls for reviewing commands, particularly those that modify or delete files.
Practical guardrails
- Work on a dedicated branch or disposable worktree with a clean starting state.
- Keep backups or recoverable history before migrations, bulk edits, or generated-code rewrites.
- Require confirmation before commands that delete, overwrite, reset, publish, deploy, or change infrastructure.
- Limit credentials and network access to what the task needs.
- Review shell commands before execution, including scripts hidden behind package-manager or build commands.
- Stop the run if the agent leaves the stated scope, reports unexplained failures, or proposes disabling a check.
Sandboxing and permissions reduce blast radius, but they do not make a bad change acceptable. You still need to understand what the command did and whether the resulting files belong in the repository.
8. Make the merge gate explicitly human-owned
AI review can suggest possible defects, missing tests, or security concerns. Use those suggestions as another input, then verify each one. GitHub says Copilot code review should supplement human review, and its Autofix suggestions are proposed changes that require explicit developer review and acceptance.
The person approving the merge should be able to explain the requirement, the important design choice, the tests that demonstrate it, and the remaining risks. If nobody can do that, the change is not ready, even when every automated check is green.
A merge checklist
- The diff contains only the intended files and behavior.
- Acceptance criteria map to tests or another observable verification.
- Architecture, naming, error handling, and dependencies match project conventions.
- Security and privacy implications have been reviewed with the usual tools and process.
- Commands, migrations, and generated artifacts have been inspected.
- A human reviewer has verified the result and consciously accepted residual risk.
Common failure modes and recovery
| Symptom | Likely cause | Recovery |
|---|---|---|
| The patch passes a narrow test but breaks another workflow. | The task was evaluated locally without tracing callers or system-level contracts. | Map the changed symbols to their callers, add a failing regression case for the broken workflow, and ask for a smaller patch that preserves the public behavior. |
| The agent edits many unrelated files. | Scope and boundaries were not explicit, or stale context encouraged cleanup. | Reset or restore the unrelated changes, restate the allowed files and out-of-scope work, and rerun from a clean branch. |
| The explanation sounds certain but the code is incomplete. | The assistant generated a plausible narrative without enough context or verification. | Ask it to list assumptions and unknowns, then verify each claim against source, tests, and command output. |
| Tests are green but security review finds a concern. | Functional tests did not exercise trust boundaries or abuse cases. | Keep the security finding as a release blocker until the normal secure-coding review and remediation are complete. |
| The agent proposes disabling a lint, type, or security check. | The check exposed a real incompatibility or the agent is optimizing for a green run. | Do not bypass the check silently. Diagnose the failure, fix the implementation or obtain an explicitly reviewed exception. |
| A refactor adds layers without reducing complexity. | The AI optimized for an abstract pattern rather than the repository’s design. | Compare the before-and-after dependency graph and remove abstractions that do not improve a stated quality attribute. |
Use a feedback loop, not a single giant prompt
Iterative work keeps failures understandable. Give the AI one task, inspect the plan or patch, run a check, and then provide the specific failure as new context. Avoid asking it to repair an entire unknown system after a vague “make it production-ready” instruction.
- State one behavior and its boundaries.
- Ask for a plan or a small implementation.
- Review the proposed files and assumptions.
- Run the relevant check and capture the exact failure.
- Ask for a targeted correction that preserves the acceptance criteria.
- Review the new diff, rerun validation, and stop when the task is complete.
When a correction fails twice, change the method: inspect the design yourself, narrow the reproducer, or implement the critical section manually. Repeating broader prompts usually creates more code and less clarity.
Keep the codebase understandable after the AI leaves
Maintainability is the long-term antidote to spaghetti code. Prefer small changes that fit existing boundaries, explicit names, straightforward control flow, and tests that describe business behavior. Delete abandoned attempts, temporary compatibility branches, and generated clutter before merging.
For design fundamentals, John Ousterhout’s A Philosophy of Software Design, 2nd Edition is a general software-design book rather than an AI-coding manual. Stanford lists the second edition as released in July 2021 and describes its focus on decomposition and managing complexity: Stanford author page. Bibliographic and book-description details are also available from Google Books. Use those principles to judge whether an AI-generated abstraction makes a system easier to reason about, not to justify adding abstraction for its own sake.
How to tell whether your process is working
Measure control, not lines of generated code. A healthy workflow makes review easier over time: tasks stay small, changed-file counts remain explainable, regressions are caught before merge, and developers can describe why each abstraction exists. If AI usage increases rework, review time, flaky tests, or unexplained dependencies, reduce autonomy and improve task boundaries before asking for more output.
- Can a reviewer identify the requested behavior from the issue and acceptance criteria?
- Can every changed file be explained in relation to that behavior?
- Did an independent test or check validate the important paths?
- Were security, privacy, and destructive-command risks considered explicitly?
- Would another developer understand the code without knowing that an AI wrote it?
The goal is not to avoid AI-generated code. It is to make delegation reversible, evidence-based, and subordinate to the project’s design and quality standards.
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