The rise of tools that generate code with artificial intelligence (AI) significantly speeds up the software development stage, but in practice business projects run into specific challenges around the quality, security and compatibility of that code. AI tools often deliver code that “works” yet hides bugs, ignores the application context, produces suboptimal solutions or introduces technical debt. That is why a professional development environment needs a systematic approach to verifying, testing and fixing AI code, so that the final product is stable, secure and in line with what the client expects.
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The most common problems in AI-generated code
Code generated by AI tools can contain various problems that, in a real project, affect its quality and security. The most frequently encountered include:
- no handling of edge cases and input errors,
- non-compliance with application security standards,
- use of outdated or discouraged technologies and libraries,
- code that runs locally but is unstable in the production environment,
- missing unit or integration tests.
On top of that, external analyses show that a significant share of AI-generated code contains critical security flaws – as much as almost half of such fragments may fail to meet minimum security standards.
These problems are not merely a theoretical risk. In practice, without proper developer oversight, an application can reach production with business logic errors or vulnerabilities that expose the client to financial losses and reputational damage.
The role of the human team in fixing AI code
Regardless of technological progress, AI will not replace experienced engineers. As industry material points out, code-generating tools should be treated as a developer’s assistants rather than as full-fledged developers.
Above all, the human team has to:
- thoroughly understand the business and technological context of the project,
- make sure that the solutions generated by AI match the client’s requirements,
- review every piece of code for tests, security and performance,
- integrate the fixes in a way that is consistent with the application architecture.
The experienced Web Systems team uses AI as a supporting tool, but it is the developers who are accountable for the final quality of the implementation, the testing and the rollout. This approach gives clients predictable application behavior and no unexpected bugs after the product is released.
Systematic methods for improving AI-generated code
To remove problems from AI-generated code effectively, it is worth applying proven development techniques that Web Systems uses in its daily work:
1. Providing context and working instructions for AI
Ideal collaboration with code-generating tools starts with giving them precise context. In practice this means preparing suitable “system prompts” that define how the AI tools are to work. Instructions of this kind define:
- which coding standards apply to the team,
- how to handle function input and output,
- which dependencies and libraries are acceptable,
- which tests should be generated and run.
This approach minimizes errors caused by missing context and puts the code-generating tool in the role of a helper rather than the source of the final solution.
2. Integrating scanning and testing tools
Automated code analysis tools (linters, static scanners, security tools) are the foundation of a solid QA (Quality Assurance) strategy. Web Systems integrates such tools into CI/CD pipelines, so that every piece of code, whether or not it was generated by AI, is verified against:
- security rules (e.g. detecting SQL injection, XSS),
- compliance with style rules and industry practices,
- unit and integration test coverage.
This makes it possible to catch potential bugs and vulnerabilities before the team even reviews the code.
3. Manual inspection and code review
Despite the growing role of automated tools, human code review remains a key element of quality control. A good code review takes into account:
- the business logic,
- whether the implementation matches the architecture,
- readability and long-term maintainability of the code.
Web Systems applies code review practices that hold AI-generated code to exactly the same standard as hand-written code. The assessment takes technical risks and alignment with the client’s requirements into account, not merely whether “the code works”.
4. Performance and security testing
AI often generates functional code that may nevertheless be suboptimal in terms of performance or security. Professional performance tests (e.g. benchmarks, load tests) help detect:
- inefficient loops or queries,
- code fragments that create bottlenecks,
- weak points in terms of scalability.
In addition, security scanners analyze the code for vulnerabilities that AI cannot always anticipate. Tests of this kind are a mandatory element of the processes Web Systems applies in web and application projects.
Best practices and tools that support work with AI
Modern tools supporting work with AI can significantly streamline the process of reviewing and fixing code, but using them deliberately is key. Here are examples of solutions that bring measurable benefits in a professional workflow:
- AI code review tools – tools that analyze code for errors and suggest fixes while taking the repository context into account.
- Static security analyzers – they scan code for vulnerabilities based on known attack patterns.
- Automated test integration – every commit of AI code triggers a test pipeline, which makes regressions easier to catch quickly.
Introducing such tools into the development process reduces the risk of shipping bugs into production applications and streamlines the work of development teams.
How Web Systems applies its experience with AI-generated code
At Web Systems we treat code-generating tools as a valuable part of the development ecosystem, one that nevertheless requires deliberate integration with engineering processes. The clients we deliver e-commerce, web and mobile application projects for expect not only fast prototyping but above all stable and secure solutions.
Our approach rests on:
- tight integration of CI/CD practices with quality verification tools,
- rigorous code review carried out by senior developers,
- comprehensive testing of every code change,
- taking real business scenarios into account when designing solutions.
This translates into higher quality of the final software and minimizes technological risk for the client.
Summary
AI tools for generating code are meaningful support in a software project, speeding up many routine tasks, yet using them requires mature processes for quality control, testing and fixing the generated code. AI does not replace programmers – rather, it raises the efficiency of teams that know how to harness its power while maintaining high engineering standards. In professional e-commerce and web application projects, such as those delivered by Web Systems, effective collaboration between people and AI translates into products that are not only delivered faster but also more stable, more secure and better matched to business needs.
