You can spot a vibe-coded codebase before you buy or inherit it by looking for a specific combination of structural red flags: no tests, no documentation, inconsistent naming conventions, bloated functions, and logic that clearly solves one narrow problem without accounting for edge cases. These patterns reveal code that was generated quickly, accepted uncritically, and never properly reviewed. The sections below walk through each diagnostic question so you can assess any codebase with confidence before committing to it. If you want to know more about how we approach code quality at Bloom Group, you will find that context useful as well.
What makes a codebase ‘vibe-coded’ in the first place?
A vibe-coded codebase is one where code was written primarily by prompting an AI tool and accepting its output without critical evaluation, architectural thinking, or engineering discipline. The term captures the practice of coding by feel rather than by design, where the goal is getting something to work right now rather than building something that holds up over time.
Vibe coding became a widely recognized phenomenon in 2025 and has accelerated into 2026 as AI code generation tools became fast enough and capable enough that non-engineers and time-pressured developers began using them to ship entire features or products. The problem is not that AI was used. The problem is that the output was treated as finished rather than as a draft requiring engineering judgment.
A vibe-coded codebase typically shares these characteristics:
- Code that solves the happy path but ignores failure modes
- No consistent architecture or separation of concerns
- Logic that is difficult to trace because it was generated in isolated prompts rather than designed as a system
- Dependencies added opportunistically, not evaluated for security or longevity
- No evidence of deliberate decision-making in commit history or comments
What are the most common warning signs in a vibe-coded codebase?
The most common warning signs in a vibe-coded codebase are the absence of tests, inconsistent naming and structure, functions that do far too much, and comments that describe what the code does rather than why it does it. These signals point to code that was never designed to be maintained.
Beyond those top-level indicators, watch for:
- Zero or near-zero test coverage: Vibe-coded projects are almost always untested because AI tools generate working code faster than test suites, and the developer never went back to add them.
- Massive functions or components: When code is generated in response to prompts, it tends to accumulate logic in one place rather than being decomposed into reusable, single-responsibility units.
- Duplicate logic across files: Without a coherent architecture, the same problem gets solved in multiple places because each prompt was answered independently.
- Inconsistent error handling: Some errors are caught and logged; others silently fail. This inconsistency reveals that error handling was never planned, only occasionally remembered.
- Hardcoded values and secrets: API keys, environment-specific URLs, and magic numbers embedded directly in source code are a classic sign of code that was written to demonstrate something rather than to run in production.
- No README or outdated documentation: If the documentation does not match the current state of the code, or does not exist at all, the project was never treated as a long-term asset.
How do you audit a codebase for vibe-coding before acquisition?
To audit a codebase for vibe-coding before acquisition, run a structured technical due diligence process that covers test coverage, dependency health, code complexity metrics, commit history, and architecture documentation. This does not require running the software in production. It requires methodical inspection of the repository itself.
A practical audit follows these steps:
- Review the commit history: Look at commit frequency, message quality, and authorship. A codebase where hundreds of commits happened in a few days with messages like “fix” or “update” suggests rapid AI-assisted generation with minimal review.
- Run a static analysis tool: Tools like SonarQube, ESLint, or language-specific linters will surface complexity scores, duplication rates, and code smell density. High scores across the board are a strong signal.
- Check test coverage: Pull the repository and run the test suite. If there is no test suite, that is itself the finding. If there is one, check whether the tests are meaningful or whether they only test trivial cases.
- Inspect dependency lists: Look for outdated packages, packages with known vulnerabilities, and unnecessary dependencies. Vibe-coded projects often accumulate dependencies because each prompt suggested a new library.
- Ask for architecture documentation: Request a system diagram or architecture decision records. If none exist, ask the current team to explain the system verbally. Vague or inconsistent explanations reveal that the architecture was never consciously designed.
- Read the most complex files carefully: Identify the five largest or most frequently modified files and read them. Pay attention to whether the logic is coherent and whether you can follow the reasoning without external context.
Which parts of a codebase are most likely to hide vibe-coded problems?
The parts of a codebase most likely to hide vibe-coded problems are authentication and authorization logic, data validation layers, error handling and logging infrastructure, and any integration code connecting to third-party services. These areas are where edge cases matter most and where AI-generated code tends to be the most superficially correct but structurally fragile.
Authentication code is particularly dangerous because it often looks functional during a demo but fails to account for token expiry, session invalidation, or privilege escalation scenarios. Data validation is similarly risky. Vibe-coded validators often check for the expected input format but do not handle malformed data, injection attempts, or boundary conditions.
Integration code connecting to external APIs is another high-risk area. AI tools generate integration code that works under normal conditions but rarely handles rate limiting, API versioning, authentication failures, or partial response scenarios gracefully. These failures only surface in production under real load or when the external service behaves unexpectedly.
Business logic buried inside UI components is a fourth area to scrutinize carefully. In vibe-coded frontend projects especially, it is common to find pricing calculations, permission checks, or state management logic embedded directly inside rendering code rather than abstracted into testable, reusable modules.
What’s the difference between vibe-coded code and AI-assisted code written by experienced developers?
The difference between vibe-coded code and AI-assisted code written by experienced developers is intent, review, and architectural ownership. Experienced developers use AI tools to accelerate implementation of decisions they have already made. Vibe coders use AI tools to make decisions for them and accept the output without evaluation.
In practice, this distinction shows up in several observable ways:
- Test presence: Experienced developers who use AI assistance still write or generate tests for their code. Vibe-coded projects typically have none.
- Architectural coherence: AI-assisted code from experienced developers fits into a deliberate structure. Vibe-coded output accumulates without a guiding architecture.
- Commit messages and pull requests: Experienced developers document their reasoning. Vibe-coded repositories often have no pull request history or review process at all.
- Edge case handling: Experienced developers anticipate failure modes. Vibe-coded functions typically handle only the case that was described in the prompt.
- Dependency discipline: Experienced developers evaluate libraries before adding them. Vibe-coded projects add whatever the AI suggested without scrutiny.
The underlying principle is that AI is a tool, not an engineer. When an experienced engineer uses that tool, their judgment shapes the output. When vibe coding happens, the tool’s output shapes the product, and the results reflect the tool’s limitations rather than engineering expertise.
Should you refactor or replace a vibe-coded codebase?
Whether to refactor or replace a vibe-coded codebase depends on the ratio of recoverable architecture to structural debt. If the core data models are sound, the business logic is mostly correct, and the problems are concentrated in specific layers, refactoring is often the faster path. If the architecture is incoherent throughout and the codebase has no tests to anchor a refactor safely, replacement is usually the more honest choice.
A useful framework for making this decision:
- Refactor if: The domain logic is correct and well-understood, the data model is stable, test coverage can be added incrementally, and the team maintaining it can do so without constant firefighting.
- Replace if: The architecture cannot support the required scale, security vulnerabilities are embedded throughout, the codebase is not understood by anyone currently on the team, or the cost of adding tests exceeds the cost of rebuilding with proper engineering discipline from the start.
One important caution: the instinct to replace is often stronger than it should be, especially for new teams inheriting old code. A full replacement carries its own risks, including recreating the same business logic incorrectly and losing institutional knowledge embedded in the existing system. A phased refactor with strict quality gates is often the safer path when there is any recoverable value in the existing codebase.
How Bloom Group helps with vibe-coded codebase challenges
We work with mid-size and large enterprises that frequently encounter exactly this situation: a product built quickly, a codebase with unclear ownership, and a decision to make about what comes next. Our team of developers, all holding advanced degrees in Computer Science, AI, Mathematics, or related fields, brings the engineering discipline that vibe-coded projects lack from the start.
Here is how we help organizations navigate vibe-coded codebases:
- Technical due diligence: We conduct structured audits of codebases ahead of acquisition or major investment decisions, giving you a clear picture of what you are inheriting.
- Refactor planning: When a codebase has recoverable value, we build a phased refactoring roadmap that adds test coverage, restructures architecture, and reduces technical debt without stopping product delivery.
- Greenfield rebuilds: When replacement is the right call, we design and build from a clean foundation using modern development methodologies, cloud-native architecture, and proper engineering discipline.
- Team as a Service: For organizations that need embedded engineering capacity rather than a one-time engagement, our TaaS model provides dedicated developers who integrate with your team and maintain quality standards over time.
If you are evaluating a codebase right now or planning a digital transformation initiative in 2026, we are ready to help you make the right call. Contact us to discuss your situation and find out how we can support your next step.
Frequently Asked Questions
How long does a proper technical due diligence audit of a vibe-coded codebase typically take?
A thorough audit of a small-to-medium codebase (under 100,000 lines of code) typically takes between three and five business days when conducted by an experienced engineering team. Larger or more complex codebases can take one to two weeks. Rushing this process is one of the most common and costly mistakes organizations make before acquisition — the findings from a proper audit almost always influence the deal terms or the decision to proceed at all.
Can a vibe-coded codebase pass basic security scans and still be dangerous?
Yes, and this is one of the most important misconceptions to address. Automated security scanners check for known vulnerability patterns in dependencies and common code weaknesses, but they do not evaluate architectural security decisions. A vibe-coded codebase can have zero flagged CVEs and still have deeply flawed authentication logic, missing authorization checks, or data validation gaps that expose the application to real attacks. Automated scans are a starting point, not a verdict.
What's the fastest way to get started assessing a codebase if I only have a few hours?
If time is limited, prioritize three things: run a static analysis tool to get a complexity and duplication score, check whether a test suite exists and how much of the codebase it covers, and read the five largest or most frequently changed files manually. These three steps will surface the most critical signals within a few hours. If all three come back clean, the codebase is likely in reasonable shape; if all three raise red flags, you have your answer without needing a deeper dive.
How do I handle a situation where the original developer insists the codebase is 'production-ready' but the audit says otherwise?
Anchor the conversation in objective metrics rather than opinions. Present specific findings: test coverage percentage, complexity scores from static analysis tools, concrete examples of missing error handling or hardcoded secrets, and a count of outdated or vulnerable dependencies. Developers who built vibe-coded projects often genuinely believe their code is solid because it worked during development — the gap between 'it works in a demo' and 'it holds up in production under real conditions' is exactly what the audit is designed to reveal.
Is it possible to introduce proper testing into a vibe-coded codebase without breaking existing functionality?
Yes, but it requires a disciplined approach. The safest strategy is to start with high-level integration or end-to-end tests that document the current behavior of the system before touching any implementation code — this gives you a safety net before refactoring begins. From there, you can work inward toward unit tests as you decompose bloated functions into smaller, testable units. Trying to add unit tests directly to untested, tightly coupled vibe-coded code without this outer safety net first is a common mistake that often leads to regressions.
What questions should I ask a development team to quickly gauge whether their codebase was vibe-coded?
Ask them to walk you through how a specific edge case is handled — for example, what happens when an external API call fails, or how the system responds to an invalid user input. Then ask why a particular architectural decision was made. Experienced engineering teams can answer both questions concretely and consistently. Vibe-coded teams often struggle with the 'why' and give vague or inconsistent answers about edge case handling, because those decisions were never consciously made — they were left to whatever the AI generated.
Are there any scenarios where a vibe-coded codebase is actually acceptable to inherit or acquire?
Yes — context matters significantly. If you are acquiring a very early-stage product primarily for its customer base, market position, or underlying data rather than its technology, the state of the codebase is less critical as long as you budget for a rebuild. Similarly, if the codebase is a small internal tool with limited scope, low security requirements, and no plans to scale, the cost of a full re-engineering effort may not be justified. The key is making the decision with full awareness of what you are inheriting, not discovering the debt after the fact.