20 steps vibe coders skip (and why each one bites back)
Vibe coding is genuinely fast. You can go from idea to working prototype in an afternoon. That speed is the whole point, and it’s real.
But speed has always had a shadow side in software development. The difference now is that AI makes the shadow harder to see. When a prototype appears in minutes, skipping the careful parts feels like efficiency, not risk. It isn’t.
According to Veracode’s 2026 GenAI Code Security Report, AI-generated code passed security tests only 56% of the time. Nearly half failed. A Purdue study of more than 117,000 dependency changes found AI agents pick vulnerable package versions more often than humans, and nearly 37% of those bad picks required a major-version upgrade to fix, compared to just under 13% for humans.
The Forbes Technology Council asked practitioners which steps teams skip when vibe coding and what each shortcut costs. Here is what they said.
Before you write a single line
1. Identify the actual customer problem
When AI makes coding fast, teams skip the hard work of understanding what the product truly needs to do. Software ships quickly but misses the mark. According to Ashutosh Madeshiya of Axiarete AI, speed without problem clarity produces software that moves fast in the wrong direction.
2. Decide whether the product is worth building at all
Jehan Hamedi of Vizit puts it plainly: when execution becomes instant, the costliest mistake is no longer building too slowly. It’s scaling the wrong idea faster. Confirming fit before building is still the cheapest validation you can run.
3. Clarify what you’re optimizing for
Speed, quality, and scale pull in different directions. Without explicit clarity on which one matters most, AI-generated code can produce something that demos well but fails to deliver long-term business value or grow beyond a proof of concept. Vishal Saxena of Octus recommends embedding design patterns directly into prompts once the team has agreed on the optimization target.
4. Define acceptance criteria before generating code
When a prototype appears in minutes, writing down what “correct” means feels like bureaucracy. Anthony Spadaro of Vantomic Inc. points out the compounding cost: once the codebase grows beyond anyone’s mental model, every AI-generated fix can silently break something else, and nobody can tell an improvement from a regression.
️ Architecture and design
5. Review the architecture before letting an agent loose
Someone still needs to decide how new functionality fits into the existing system. Elton Chan of Second Talent warns that if those decisions happen during implementation, they get buried in generated code and become harder to unwind. Settle the architecture first. Let AI handle the implementation.
6. Design features and user experience intentionally
Because execution is cheaper, it’s tempting to skip deliberate design and just iterate on whatever AI produces. Rohit Patel of ndoc.ai flags the creativity cost: once you’re committed to a particular execution path, going back to explore other approaches gets expensive fast.
7. Plan for extensibility and maintenance
Vibe coding tends to look like continuously adding parts with limited refinement of overall code quality, according to Nolan Garrett of TorchLight. Future maintainability is rarely what AI optimizes for on its own.
Security
8. Run a code security review
Security reviews are the first thing to go when deadlines are tight. Alma Jones of Comerica Bank cites the Veracode 2026 data directly: AI-generated code passed security tests only 56% of the time. Catching a flaw during review is a quick fix. Catching it in production is an incident.
9. Don’t let reviewers assume the output is secure
Most teams still review AI-generated code. The problem, as Andrea Scaduto of SecureFlag describes it, is that teams assume every reviewer can distinguish secure output from insecure output. That skill is unevenly held. AI writes code that looks right far more often than it writes code that is right, so a quick read confirms the happy path and misses the auth check.
10. Run adversarial security tests before release
Vibe coding doesn’t change the vulnerability cycle, it just speeds it up. Adriel Desautels of Netragard points out that developers create vulnerabilities and researchers exploit them. That has always been true. Without adversarial testing before release, structural flaws get embedded in production architecture, and removing them after a breach costs far more than preventing them.
11. Protect sensitive data in prompts
In the rush to move fast, sensitive or confidential information can end up in AI prompts. Prashanthi Kolluru of KloudPortal notes that this shortcut creates privacy, security, and compliance risks that surface later, not immediately.
12. Scope what the AI agent can touch
Coding assistants often run with the developer’s own credentials. An app built in an afternoon can inherit production database access and connections to unvetted MCP servers. Linus Hakansson of Gravitee recommends giving each agent its own narrowly scoped identity and an audit trail from day one. Retrofitting permissions after launch is slow and expensive, and one bad command can wipe live data.
Dependencies and packages
13. Vet open-source packages before merging
AI pulls in open-source packages as a shortcut to build features. Aaron Mitchell of HeroDevs warns that without active review of those packages, including support health and actual functionality, tech debt accumulates and new security issues get introduced unnecessarily.
14. Review dependency versions specifically
The Purdue study of more than 117,000 dependency changes found AI agents pick vulnerable package versions more often than humans. Nearly 37% of those bad picks required a major-version upgrade to fix, compared to just under 13% for humans. Steve Carter of Nucleus Security’s practical rule: let AI write code, but don’t let it pick dependencies alone.
Testing and quality
15. Enforce your own coding standards
The team that maintains the code and fixes defects is not the AI. Buyan Thyagarajan of Eigen X recommends evaluating generated code against internal coding standards, using automated testing to prevent bugs, and using an LLM to document the code architecture (data model, patterns) for long-term maintenance.
16. Test AI-generated code rigorously
The challenge is velocity. Traditional testing often can’t keep pace with how quickly AI creates code, and developers want to move fast to production. Shubha Govil of Sauce Labs points out that ignoring proper testing creates blind spots that compound into reliability, security, and quality issues. Teams need testing approaches that can scale alongside AI-driven development.
Understanding and documentation
17. Read and understand the code after it works
Vibe coding can translate ideas into working products quickly. But if you move forward without a clear understanding of what was built, the first change or bug becomes significantly harder to deal with. Adithyan RK of Hyring.com puts it simply: speed is useful, but someone still needs to be accountable for what AI built after it has done its work.
18. Write down why the code works, not just that it does
Too often the attitude is “it ran, it looks right, ship it.” Hastimal Jangid of Coozmoo Digital Solutions describes the downstream cost: when something breaks months later, nobody, including the AI, can explain what the code was supposed to do. Skipping documentation trades a fast launch for an undebuggable mystery.
19. Question your assumptions about AI code quality
Aman Raheja of HPE frames this as a broader problem: development teams have historically skipped steps for speed, but it’s now happening faster. Many people with little or no software development background assume AI tools inherently produce good code. That assumption is the root issue, not any single skipped step.
Human feedback
20. Keep the human conversations around the code
When software is easy to generate, teams can stop sitting with users, talking through trade-offs with colleagues, and soliciting honest feedback. Lakshmanan Alagappan of Genpact UK notes that the code can look finished long before anyone has agreed on what problem it should solve. Discovering that gap after launch is costly.
The common thread across all 20 points is that AI accelerates execution, it doesn’t replace judgment. The steps above aren’t bureaucracy. They’re the parts of software development where judgment lives, and they matter more, not less, when the code itself appears instantly.
I’m founder of BizStack at Cagri Sarigoz LLC and a passionate advocate for entrepreneurs.
With over 14 years in tech, marketing, and AI, including my role as Head of SEO at CitizenShipper and co-founder of TaleBot at Intale AI, I’m dedicated to sharing genuine, useful product insights and tips.
At BizStack, I aim to cut through the digital noise to provide clear, actionable advice.
And more than all else, I’m a father to a (always) little girl and a husband.
Contact me at [email protected] for assistance.
