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What if a significant portion of the code being written today is no longer written by humans?
According to Google, AI is already responsible for generating a noticeable share of new code inside the company. At the same time, engineers at JPMorgan Chase have reported a productivity increase of up to 20% thanks to AI coding assistants.
At first glance, this sounds like the ideal scenario: faster coding, less routine work, and higher efficiency. That’s why developers are increasingly using AI to generate code, automate tasks, and speed up their workflow.
But there’s a problem that gets talked about far less. This code often doesn’t work.
Or more precisely, it works until it meets reality: unexpected inputs, real-world load, integrations, and unpredictable system behavior. That’s where AI-generated code often starts to break.
According to Statista, the AI code generation market is growing rapidly. But alongside that growth, we’re also seeing an increase in AI code problems, AI code bugs, and situations where code breaks after deployment.
In this article, we’ll explore why AI-generated code fails in real projects, the most common issues developers face, and how to build a process where AI actually helps instead of creating additional risks.
Why AI-Generated Code Fails in Real Projects
AI almost always writes code that works, as long as everything goes according to plan.
AI Generates Code for The “Happy Path” — Not Real-world Edge Cases
The so-called happy path is a scenario where the user provides correct input, the API responds without delays, and the system behaves in a perfectly predictable way. These are exactly the kinds of examples most commonly found in training data, which is why AI models reproduce them again and again.
The problem is that real-world development is not about ideal scenarios. It’s about situations where users behave unpredictably, networks fail, data arrives in unexpected formats, or processes collide in race conditions.
Lack of Context: Why LLMs Don’t Understand Your Codebase
Imagine being given a single function and asked to integrate it into a large product. But you haven’t been given access to the architecture, so you have no understanding of the dependencies or any knowledge of how the rest of the system works. You would most likely make mistakes. That’s exactly how AI works.
Even the most advanced LLMs don’t see your full codebase. They don’t know which APIs are actually used, which library versions are installed, or how different parts of the system interact. They have no access to business logic or change history — only to what’s included in the prompt.
This raises a logical question: if context is the problem, why not just provide the entire codebase? In practice, that doesn’t solve it.
First, there are context size limitations. A real product can include hundreds of thousands of lines of code, dozens of services, complex dependencies, and integrations. That volume simply doesn’t fit into a single request. In addition to that, LLM models start to hallucinate after reaching a threshold of 100-120k tokens.
Second, it’s not just about size. A codebase isn’t just text — it’s a network of relationships: architecture, module interactions, hidden dependencies, and system behavior over time. Even if you provide a large chunk of code, AI still cannot fully reconstruct that picture.
Third, context is constantly changing. APIs evolve, library versions update, and business logic shifts. AI, however, always works with a static snapshot — whatever was provided at the moment of generation.
As a result, an AI assistant continues to generate code based on a limited and partially disconnected context from reality.
Pattern Matching Is Not Real Software Engineering
The most important thing to understand is this: AI doesn’t “understand” code — it predicts it.
With the growing reliance on AI, it’s easy to forget that large language models do not think like a software engineer. They don’t analyze architecture, evaluate trade-offs, or consider system reliability. Their goal is to predict the most likely continuation based on patterns they’ve seen before. That’s what pattern matching really is.
This is why AI generates code that looks convincing. It is syntactically correct, follows familiar patterns, and often even passes basic checks. But behind that confidence, there is no real understanding.
Such code may appear correct at first glance, but deeper inspection often reveals that it doesn’t account for real system constraints, ignores complex scenarios, and cannot guarantee correct behavior.
This is where the paradox of modern vibe coding emerges: we write code faster than ever, yet spend more time debugging AI and fixing AI-generated code issues.
Common AI Coding Mistakes Developers Face
Even when AI-generated code looks clean and “correct,” in practice, it often contains typical issues developers run into again and again. These AI code problems aren’t always obvious at first, but they’re exactly what turns into bugs later — during integration or in production.
To make these patterns easier to spot, the most common issues are summarized in the table below.
| Category | What Happens | Typical Signs | Why It’s a Problem |
| Missing error handling | AI assumes ideal conditions, and skips proper error handling | No try/catch, missing validation, no fallback logic, silent failures | Errors go unnoticed, system behaves incorrectly, debugging becomes time-consuming |
| Dependency & environment mismatch | Code doesn’t align with the actual tech stack or environment | Outdated/non-existent libraries, wrong dependency versions, API mismatches | Code may not run at all or breaks during integration or deployment |
| Security vulnerabilities | AI generates code without proper security considerations or leaves credentials like passwords and API keys public | Missing input validation, unsafe queries, hardcoded secrets | Leads to risks like SQL injection, data leaks, and system compromise |
| Type and logic issues | Code is syntactically correct but logically inconsistent | Type mismatches (TypeScript), incorrect assumptions about data structures | Causes unpredictable behavior and hard-to-diagnose bugs |
Common AI Coding Mistakes
ChatGPT, Claude & Copilot Code Issues Explained
The use of popular AI tools has significantly reduced the complexity of coding. At the same time, their limitations tend to become more visible during real development.
Below are a few examples based on code generated by ChatGPT and GitHub Copilot that highlight common issues developers run into.
ChatGPT Code Issues in Real Development Workflows
ChatGPT is one of the most widely used AI assistants for generating code. It can quickly generate code, explain logic, and suggest solutions. But this is also where problems often begin.
One of the biggest issues is the so-called “hallucinations.” ChatGPT can confidently suggest non-existent APIs, invent functions, or reference methods that don’t exist in real libraries. The responses look convincing, which creates a false sense of correctness.
GitHub Copilot Problems in Large Codebases
Copilot excels at autocomplete and speeds up coding within the current file. However, its effectiveness drops as the project grows.
The main issue is that Copilot doesn’t really see the bigger picture. It works with whatever code is in front of it and builds on top of that — whether the pattern is good or not.
In large codebases, this can lead to accumulating technical debt: solutions may look correct at the line or function level but don’t align with the overall application logic and disrupt the workflow.
Claude and Anthropic Limitations in Coding
Claude is often seen as a more “thoughtful” AI. It tends to explain code better, structure responses more clearly, and provide more detailed solutions.
However, it has its own limitations. Claude may oversimplify problems by skipping important details or, on the other hand, provide overly complex solutions that require additional adaptation, leveraging the overall cost of the infrastructure needed.
In the context of Claude code, this means the output often looks polished but still needs careful review — key parts may be missing, and the implementation may not fully match the actual requirements.
AI Coding Assistants vs Real Coding Agents
It’s important to distinguish between AI coding assistants and full-fledged coding agents.
Tools like Copilot or ChatGPT primarily offer suggestions and help developers write code faster. More advanced tools, such as Cursor or Claude Code, aim to act more like coding agents — analyzing tasks and generating broader changes.
However, even these AI coding tools remain limited. They don’t make architectural decisions, don’t take responsibility for outcomes, and can’t guarantee correctness in complex systems.
In the end, regardless of the tool, AI remains an assistant — not a replacement for a developer.
Debugging AI-Generated Code: What Actually Works
When AI-generated code starts to break, one thing becomes clear: getting AI to write the code is only half the job. The other half is debugging AI — and that part often takes longer.
The challenge is that the usual ways developers debug code do not always work as effectively with AI-generated output. What helps here is a more structured and careful process.
Why Debugging AI Code Is Harder Than Writing It
Generating code with AI can take minutes. Figuring out why it doesn’t work can take much longer.
The main reason is simple: AI does not explain its reasoning. It doesn’t show what assumptions it made, what decisions it took, or where it may have gone wrong. Unlike a human developer, it leaves no thought process you can follow.
As a result, debugging AI-generated code often feels like dealing with a black box. The code may look perfectly reasonable and still behave in the wrong way — and it’s not obvious where the problem actually is.
That makes AI-generated code issues harder to diagnose than bugs in code written by a developer.
Step-by-Step Workflow for Debugging AI-generated Code
To debug this kind of code effectively, it helps to resist the urge to fix everything at once and work step by step instead.
First, reproduce the issue and make sure the failure happens consistently. Then isolate the part of the code where the problem appears and remove unnecessary context. After that, check the key assumptions: whether the data is correct, whether the API behaves as expected, and whether the types and logic still make sense.
Only then does it make sense to change the code and try to fix bugs.
This kind of workflow turns chaotic debugging into a more controlled process and helps you find the real cause of the issue instead of just patching the symptoms.
Using Scanning Tools, Linters, and Code Review
Manual debugging is only part of the solution. To improve the quality of AI-generated code, it’s important to bring in additional tools.
Linters can catch basic mistakes and flag code that does not follow standard coding practices. Scanning tools help identify vulnerabilities and risky areas in the code. And proper code review makes it possible to evaluate the solution from the perspective of architecture, maintainability, and logic.
It’s especially important to treat AI-generated code like any other code: through pull requests, with mandatory review and discussion.
That approach reduces the risk of hidden issues reaching production and makes debugging AI far more predictable and manageable.
How to Fix AI-Generated Code
If AI-generated code breaks, it doesn’t mean AI is useless — it means it’s being used the wrong way.
Most issues don’t come from the AI tool itself, but from how it’s applied. Below are practical approaches that help you actually fix AI-generated code and bring it closer to production quality.
Improve Your Prompt to Generate Better Code
The quality of the output depends directly on how the prompt is written.
The more specific and structured your request is, the higher the chance that AI will generate code that matches real requirements. Vague prompts almost always lead to generic and oversimplified solutions.
A good prompt typically includes context about the task, the tech stack being used, specific constraints (such as API or library versions), and expectations around error handling and edge cases.
In practice, the prompt acts as the interface between the developer and the AI, and the more precise it is, the fewer problems you’ll have later.
Treat AI-generated Code as a Draft, Not Final Code
AI doesn’t deliver a finished product — it gives you a draft.
The best way to think about it is as a junior developer who can quickly sketch a solution but can’t guarantee its quality. That’s why reviewing code is a mandatory step.
It’s important to check whether the solution matches the intended logic, handles data correctly, and follows established coding practices.
This approach helps avoid situations where the code “looks fine” but contains hidden issues that affect code quality.
Add Missing Pieces AI Skips
Even good AI-generated code often lacks critical components.
Most commonly, it’s missing proper error handling, coverage for edge cases, logging, and input validation. These elements are rarely generated by default, yet they are essential for making code stable and production-ready.
That’s why after generating code, it’s not enough to just fix visible issues — you also need to add what AI typically leaves out.
Build a Safe AI-assisted Coding Workflow
To get real value from AI, it needs to be part of a well-defined workflow.
This means having human oversight in place, treating AI coding assistants as tools rather than sources of truth, and integrating them into testing, code review, and CI/CD processes.
AI is great at speeding up development, but it doesn’t replace quality control. When used within a structured process instead of in an ad hoc way, it reduces AI code problems and turns AI into an advantage rather than a risk.
How SCAND Helps Fix AI-Generated Code and Build Reliable Software
Once AI-generated code is already in use, the question is usually no longer “should we use it?” but rather “how do we make it actually work?”
In practice, many teams come in with code that “almost works.” It handles basic functionality but is unstable, poorly integrated into the system, and full of hidden issues. In these cases, the goal is not just to fix AI-generated code point by point, but to bring it to a production-ready state — eliminating bugs, stabilizing behavior, adapting it to a real workflow, and rewriting critical parts where AI made incorrect assumptions.
The most effective approach is not to abandon AI, but to use it properly within an AI engineering framework. At SCAND, AI tools are treated as a way to accelerate development — not as a source of final solutions. The key role belongs to software engineers, who review the code, resolve inconsistencies, add missing logic, and bring it up to the required level of code quality.
This approach allows teams to keep the speed AI provides while avoiding typical AI code problems and improving overall system reliability.
It’s also important to recognize that AI does not cover the entire development process. Full-cycle software development still includes architecture, integrations, testing, and ongoing support. Combining AI with engineering expertise is what makes it possible to build solutions that don’t just “work for now,” but remain stable, scalable, and predictable over time.
Key Takeaways
AI-generated code has become a standard part of modern coding workflows, but without proper control, it remains unreliable. Most issues stem from a lack of context and ignored edge cases, which lead to failures in real-world conditions. Debugging AI requires a more structured approach than traditional development, as these issues are harder to trace. In practice, the best results come from using AI as a tool, while keeping key decisions and quality control in the hands of experienced developers.
