If you’re building with an AI coding agent, the fix is not simply to write a longer prompt. Give the agent the context and tests it needs, keep its work easy to inspect, and match its permissions and freedom to the risk of the software. Vibe coding spans a range of human oversight; high-level prompting with little review is only one end of it.
What “doing vibe-coding wrong” actually means
There is no single official 17-point vibe-coding method. The checklist below turns practical guidance from Microsoft’s Visual Studio Code documentation and the UK National Cyber Security Centre (NCSC) into a workflow you can apply to coding agents generally. The central idea is to make generated work easier to inspect and verify—not to assume that a prompt can guarantee correct or secure software.
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The NCSC describes a spectrum. At its high-autonomy end, a developer gives a broad or ambiguous prompt, lets the AI choose architecture, code, modules, and tests, then judges the result largely through further prompts rather than close code review. More controlled approaches include specifying modules, reviewing returned work, or writing tests manually while the AI implements against them. The right point on that spectrum depends on what the software can affect.
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The 17-point fix
1. Choose the right level of autonomy
Keep tighter human control for changes that can affect sensitive data, authentication, payments, safety, or important business operations. For a low-impact prototype, broader delegation may be reasonable. Treat autonomy as a task-by-task choice, not a permanent setting for the whole project.
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2. State the outcome and the boundaries
Describe what the feature must do, who it serves, and what it must not change. Name relevant constraints such as supported behavior or compatibility requirements. A clear target gives the agent useful context; a vague request leaves consequential choices implicit.
3. Provide project architecture and conventions
Tell the agent where relevant code lives, how the project is organized, and which conventions or instructions it should follow. Microsoft’s VS Code guidance recommends project-level instructions to supply recurring context. Include only conventions that apply, and point the agent to the files that define them.
4. Break broad work into modules
Turn a large feature into smaller, reviewable changes with clear boundaries. The NCSC identifies specifying modules as a more controlled way to work than handing an agent an entire product idea. Smaller tasks make it easier to spot when implementation choices drift from the intended design.
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5. Put acceptance tests in the prompt
Describe observable pass conditions, including important edge cases. Microsoft recommends including test cases in prompts so the AI has a concrete way to check its work. If the feature is not readily testable, define what a reviewer should observe instead.
6. Ask for a plan before a broad change
For work that spans several files or has meaningful consequences, ask the agent to outline its approach and likely files before it edits. Check the plan against the requested boundaries; correct a mistaken assumption early rather than discovering it in a large diff. This is a practical workflow recommendation, not a quoted requirement from the NCSC or Microsoft.
7. Work in checkpoints
Ask for progress in stages rather than waiting for a large change to arrive all at once. Microsoft recommends using checkpoints to review progress and rewind when the agent goes off track. A useful checkpoint is one where the result is small enough to understand before the next change begins.
8. Keep each change reviewable
Prefer focused edits over unrelated refactors bundled into the feature. Smaller changes make it easier to understand what changed and why, and to isolate a regression if something breaks. If the agent expands the scope, ask it to separate the extra work.
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9. Run relevant tests yourself
Use the project’s appropriate tests and check the behavior against the acceptance criteria. An agent’s statement that tests passed is not a substitute for verifying the result in your environment. A passing test suite is useful evidence about the cases it covers, not proof that every important behavior is correct.
10. Review the diff before accepting it
Read the actual changes rather than relying on a summary. Microsoft recommends code review on the resulting pull request. Check whether the implementation matches the intended scope, whether tests meaningfully exercise the change, and whether any unexpected files or behavior were added.
11. Look for placeholder behavior
Check whether a feature that appears complete actually uses sample data, hard-coded responses, stubbed functions, or unfinished paths. A 2026 preprint, Understanding the (In)Security of Vibe-Coded Applications, reports placeholder logic among recurring patterns in the applications it studied. That is a finding about the studied applications, not a rate that can be applied to all AI-generated software.
12. Inspect input handling
Follow user-controlled data through the application, especially where it crosses into storage, queries, commands, or external services. Look for validation and appropriate handling at those trust boundaries. The same 2026 study reports unfiltered input among recurring weaknesses in its sample; it does not establish that every vibe-coded application has this flaw.
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Check the diff and relevant configuration for credentials or other sensitive values that should not be committed or exposed. The 2026 preprint also reports secret exposure among the patterns it found. Treat that as a reason to inspect your own project, not as evidence that a particular project contains a secret.
14. Inspect agent tools and repository permissions
Before trusting an agent with a directory, find out what local commands, external services, and other tools it can use. Mistral’s guidance for its Vibe product specifically advises reviewing the repository’s .vibe/ configuration, including MCP server definitions and tool permissions, because they can permit access to external services or local commands. Those configuration details are specific to Mistral Vibe; the broader habit is to understand an agent’s permissions before granting access.
15. Treat content the agent reads as untrusted
Files, documentation, and web pages can contain instructions that conflict with your intent. Mistral’s security guidance discusses prompt-injection risks from untrusted content read during autonomous runs. Do not assume that text encountered by an agent is safe to follow merely because it appears in a repository or on a website.
16. Use specialized workflows when they fit
For recurring work, use a workflow designed for the task rather than relying on one general-purpose request. Microsoft’s VS Code documentation lists custom agents for workflows such as test-driven development (TDD) and security audit. A specialized workflow can focus the task, but its output still needs review and verification.
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A successful demo shows that one path worked under particular conditions; it does not establish that the broader feature behaves correctly or securely. The NCSC warns that minimal oversight of AI-generated code creates a real risk of security vulnerabilities. Its warning is about risk, not a claim that all generated code is insecure. Match review and verification to the consequences of failure, and do not treat a working demonstration as the only release criterion.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to apply the checklist to a real change
- Set the risk and scope: decide how much autonomy is appropriate, describe the intended outcome, and state what is out of scope.
- Supply context and a target: point to project instructions and architecture, split broad work into modules, and define acceptance tests or observable checks.
- Plan and checkpoint: request a plan for broad work, review progress as it arrives, and rewind or redirect when it departs from the goal.
- Verify the result: run relevant tests, inspect the diff, and check for unfinished behavior, unsafe input handling, or exposed secrets.
- Check access before trusting the agent: understand which tools and services it can access, and account for untrusted content it may read.
Evidence about the scale of particular risks should be read with its denominator and scope in mind. An ISACA article published July 29, 2026, reported RedAccess researchers identified more than 5,000 applications with little or no security controls or authentication; nearly 40% of those analyzed applications exposed sensitive information, including medical records, financial data, internal business documents, and customer conversation histories. The reported passage does not establish a denominator representative of all vibe-coded applications, so these figures should not be generalized to that wider population.
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