How AI-Based Code Generators Work
AI-based code generators have opened a new era in programming where functions, API queries, and prototypes can be created in a matter of minutes. GitHub Copilot, ChatGPT, CodeWhisperer and other tools are already actively integrated into developers’ workflows. They promise acceleration, automation and reduction of routine work. But behind the efficiency are hidden nuances: AI does not understand business logic, does not take into account context and is not responsible for errors. Therefore, the results of AI work always need to be checked by a person.
How AI-based Code Generators Work
- They work based on training on open repositories (for example, GitHub).
- Generate code based on statistical patterns, without understanding the problem.
- They cope well with template queries, but are weak in complex or critical scenarios.
- Often they produce not the best, but the most “probable” code.
Basic Tools
- GitHub Copilot
The most popular one integrates with VS Code, created by OpenAI.
- ChatGPT (OpenAI)
A flexible assistant: generates, analyses and explains code.
- Amazon CodeWhisperer
Focused on the corporate segment with an emphasis on security.
- Cody (Sourcegraph)
AI explanation of existing code, convenient navigation through the code base.
- Codeium
A free generator with support for over 70 languages, powerful autocompletion.
All these tools have their advantages, but the result should always be checked manually.
What AI code Generators Promise
- Speed is the generation of functions and APIs in seconds.
- Automation of routines means more time for architecture.
- Instant prototypes are MVPs without a large team.
- Helping beginners includes explanations, tips, and templates.
But the developers are responsible for quality, security, and relevance.
Potential Threats and Risks
AI-based code generators have become an indispensable tool for quickly obtaining solutions, but their convenience can hide serious risks. Without proper verification and understanding, the generated code can pose a threat to both a single project and the business as a whole.
Dangerous Templates and Vulnerable Code
AI often uses templates from open repositories, not distinguishing between outdated practices and modern standards. This can lead to vulnerabilities such as SQL injections, XSS, or unsafe input processing. For example, some generated codes from ChatGPT were found to have fragments without any validation that opened direct access to the database.
Opaque Dependencies or License Violations
AI can generate code that partially or completely copies other people’s libraries or fragments with license restrictions. This is especially critical for commercial projects, as using code without adhering to the terms of a license (e.g. GPL or MIT) can lead to legal consequences.
Lack of Optimisation For a Specific Project
The generator does not take into account the context of the business logic or infrastructure. For example, the code may work in a vacuum, but not be adapted to caching, multi-user scenarios, or the APIs that your site uses. Such “generic solutions” can negatively affect performance.
Poor readability, Lack of Documentation
AI code often looks like a black box: without comments, with unintuitive variable names, and without following design rules. This makes it difficult to maintain, review, and introduce new developers to the project. Ultimately, this turns into a debt that is difficult to service.
The Psychological Trap of “Everything is Ready”
The user receives code that looks convincing, compiles, and even partially works. This creates a false sense of completion when in reality, the code needs testing, security checks, optimisation, and adaptation to the needs of the project. Developers or managers may underestimate the amount of work, relying on the illusion of a ready-made solution.
Typical AI Code Problems that Devfan Finds in Projects
The Devfan team regularly encounters cases where clients bring in generated AI code with a request to “just integrate”. But even a quick audit shows that such fragments are rarely ready for use without significant refinement.
The Code does not Meet Standards
AI does not always follow the rules of writing code accepted in the company or industry. This applies to both stylistics and functional aspects: security, extensibility and performance. We often see examples where there is no CSRF protection, where caching is not used or data is intentionally “hardcoded”.
Errors that are Difficult to Detect without a Deep Review
AI is able to create code that looks correct, but contains logical errors, atypical dependencies or hidden conflicts with other modules. For example, a function may return the wrong data type or violate asynchronous calls. Such errors are not immediately obvious and may only appear in complex scenarios.
Code Does not Scale
Many AI-generated solutions do not anticipate the growth of the load or the number of users. For example, the structure may not be optimised for working with large amounts of data, may not use pagination, and may not have asynchronous query processing. In the future, this will lead to a complete rewrite of the architecture.
Excessive or Duplicated Functionality
Artificial intelligence often offers functions that are already implemented in existing libraries or frameworks or duplicates parts of the logic. This creates “bloated” code, increases development time and maintenance complexity. Devfan often optimises such fragments, reducing the amount of code without losing functionality.
→ Read more: Artificial Intelligence and the Human Factor: Why AI Proofreading Has Become a New Profession
How Devfan Works with AI-Generated Code
Instead of completely abandoning AI-generated code, the experienced team at Devfan take a systematic approach to testing and improving it. After all, the main thing is the quality, long-term effectiveness of the solution and speed, of course.
Audit: Analysing the Code for Errors and Inconsistencies
The first stage of working with AI code is a technical audit. Our specialists check the code for compliance with security, performance and architectural integrity standards. It is often at this stage that hidden errors, vulnerabilities or unjustified dependencies are discovered that can lead to problems in production.
Rewriting and Optimisation
AI generators often create code that works “somehow”, but is far from optimal. We rewrite such fragments, simplify logic, eliminate duplication and add missing elements that are critical for scalability, security and ease of further support.
Performance and Security Improvements
One of our key tasks is not just to rescue software, but to make it faster, more resilient, and less vulnerable to attacks. We implement caching mechanisms, XSS and CSRF protection, ensure proper database work, encryption, and access control.
Documentation and Preparation for Support
AI code rarely includes supporting documentation. This means that any novice developer will spend hours trying to figure out what each function does. We create readable documentation, add comments to critical areas, and prepare the project for further scaling or handover to another team.
When to Avoid Using AI Code Generators
Despite the obvious benefits, there are situations where using AI as a primary development tool can do more harm than good. Especially when it comes to complex or sensitive projects.
Critical Projects
In industries where security, accountability, and accuracy are paramount, such as finance, medicine, and data protection, using AI code without deep validation is unacceptable. Even a single, unobvious mistake can have serious consequences.
Architecturally Complex Solutions
AI does not understand the system architecture at a high level. It cannot assess dependencies between modules, design constraints, business context, or user integrations. If the project involves multi-tiered architecture, microservices, or logic that interacts with a custom API, it is best to bring in an architecture specialist.
Unique Business Logic That AI Can’t Guess
Even the smartest AI doesn’t know exactly how your business works. If your functionality is different from the “typical cases” or has special rules for processing, validation, or user interaction, you should not rely on automatic generation. You will end up with a set of template solutions that do not take into account your goals.
How to Effectively Use AI in Development
Artificial intelligence can be a powerful tool if used wisely. It does not replace the developer, but it helps a lot at certain stages.
A Tool for Drafts, not for Final Code
AI code generators are best used as generators of ideas or drafts. For example, to create a basic class structure, generate mock data, or a function template.
AI to Generate Templates, not Business Logic
You should not rely on AI to write the core functionality of your business. It is better to use it for typical solutions:
- form validation
- pagination
- basic display logic
- CRUD operations.
Mandatory Human Review
Every line of code should be reviewed by an experienced developer. This is a basic requirement for secure development in 2025. Even the best AI makes mistakes or suggests outdated practices.
Security and Performance Review
Performance audits, load testing, security analysis, and license and dependency checks are required before deployment. This is something that AI cannot do well yet. Therefore, it is the responsibility of the team.
Why Devfan is a Reliable Partner for Working with Code and AI
Artificial intelligence is changing approaches to programming, and more and more companies are starting to integrate tools like GitHub Copilot, ChatGPT, Cody, and others into their workflows. At Devfan, we have direct practical experience with such generators and know exactly how to turn their capabilities into value for real businesses. We are successfully combining its speed with the knowledge, logic, and technical expertise of our engineers in AI solutions.
Experience with Various AI Generators
Our team has repeatedly worked on projects where the code was partially or completely created using AI. We have experience interacting with popular tools and a deep understanding of their algorithms, which allows us to quickly identify both advantages and critical weaknesses in the code.
Combining AI Speed with Human Experience
We do not use AI blindly. Each solution proposed by AI undergoes human evaluation, technical verification, and adaptation to the specific needs of the project. This approach allows you to reduce the time spent on routine tasks without sacrificing quality.
Thorough Code Analysis and Rewriting
Our specialists conduct a full audit, optimise logic, improve architecture, eliminate duplication, fix standard violations, and make the code readable.
High Level of Security and Performance
Security is one of the key areas of verification. We identify potential vulnerabilities that AI may miss and implement reliable solutions that meet modern performance and data protection requirements.
Documentation and Support
Generated code rarely has documentation. We refine it from scratch so that the support team or other developers can easily understand how everything works and quickly make changes in the future.
Flexibility to Meet Business Needs
We adapt the code from a technical point of view, according to the project goals, so that the product works effectively in a real environment, and not just “on paper”.
Technological Maturity of the Product
As a result, the client receives a full-fledged, technologically mature system component, ready for scaling, development and uninterrupted operation.
Reliable Partnership, not a One-Time Solution
Devfan is a team that accompanies the client at all stages of working with artificial intelligence: from assessment to support. We think strategically, and this is what sets us apart from many.
Conclusions: How to Work with AI Code Without Risk
Artificial intelligence complements, not replaces humans. And for the use of AI in development to bring real value, it is important to generate code, to test, adapt and improve it, taking into account real business needs.
Here are the key conclusions to consider:
- AI is a tool, not a developer.
It works based on statistics and patterns, not logic, context or engineering thinking.
- The illusion of a “finished product” is dangerous.
AI can create code that looks convincing, but has critical flaws or even hidden vulnerabilities.
- Human control is mandatory.
Only an experienced developer can identify weaknesses, optimise the architecture and ensure compliance with standards.
- Devfan is your experienced partner.
We turn AI drafts into high-quality, scalable, and secure solutions that can withstand real-world workloads.
- The future is in the symbiosis of AI + people.
AI takes over the routine, and people take over strategic decisions, security, quality control, and product vision.
- We help turn unstable AI code into production solutions.
With Devfan, your product goes from an “experiment” to a technological reality ready for growth.
AI for Code Generation: Frequently Asked Questions
❓ Can AI be fully trusted for commercial projects?
No. AI can help, but it does not replace full-fledged development. It does not understand your business, does not see architectural dependencies, and does not take into account all risks.
❓ How long does a code review after AI take place?
Depending on the complexity. On average, from a few hours to a few days. The more critical areas and non-standard solutions, the longer the review takes.
❓ How is AI code different from junior developer code?
A junior developer understands context but has limited experience. AI has no context but has a “vision” of thousands of solutions. However, both require validation.
❓ How do you know that AI code needs optimisation?
When the code is unreadable, slow, or has a lot of duplicate elements. Other signals include unstable performance or difficulty scaling.
❓ Can AI create a project architecture?
No. It can generate templates, but it will not build a full-fledged architecture taking into account dependencies, security, and business logic.
❓ What types of code are least secure when generated by AI?
Authentication, payment processing, access control, and working with APIs. Human control is needed here.
❓ Should AI be used to generate tests?
Yes, as a starting point. But tests written by AI are often superficial and do not cover extreme cases.
❓ Can AI violate license rights?
Yes. If the code is generated based on someone else’s licensed code, this can create legal risks.
❓ Can AI be used to write documentation?
Yes, for drafts. But the final version should be reviewed by a technical writer or developer.
❓ How do you integrate AI code into an existing project?
Only after auditing, testing, and verifying that it meets your team’s standards.
❓ Can AI create a user interface?
It can create a template or prototype. But without a designer, the interface will remain raw or unusable.
❓ Is there a difference between Copilot and ChatGPT in code generation?
Yes. Copilot is more IDE-friendly and “fits” into the context, while ChatGPT generates code based on a more general dialogue.
❓ How to avoid the dangers of using AI code?
Always review, add tests, check dependencies, and document changed parts.
❓ Should you use AI for MVP?
Yes, but with caution. It’s good for a quick prototype, but requires in-depth testing before production.
❓ Does Devfan use AI in their work?
Yes, we use it as an auxiliary tool, but only under the supervision of developers and with mandatory auditing of each solution.
