How to Use GPT-4 for Analyzing and Improving Drupal Code

Deep Analysis of AI Use for Refactoring and Optimizing Legacy Drupal Code

Imagine a Drupal site that started its journey on version 7, then migrated to Drupal 8, and later to Drupal 9. Such projects inevitably accumulate technical debt: outdated functions, suboptimal SQL queries, code duplication, and many other issues. Manual auditing of such a project could take weeks, if not months. This is where GPT-4 comes to the rescue.

When we started using GPT-4 for code analysis, we were impressed by its ability to quickly identify problematic areas. It instantly found deprecated functions, suggested modern alternatives, and could even transform procedural code into object-oriented code. Its assistance with optimizing SQL queries—one of the biggest sources of performance issues in Drupal projects—proved especially valuable.

"Initially, we were skeptical about AI's ability to understand Drupal specifics," recalls our technical director. "But the first analysis results were so accurate that we were amazed. GPT-4 didn't just find problems but gave detailed explanations of why certain approaches were suboptimal and how they could be improved."

We called our approach "recursive optimization" because it involves a cyclical process where each subsequent step builds on the results of the previous one. We started by analyzing the project structure, then received recommendations from GPT-4, evaluated them, implemented the most appropriate ones, analyzed the results, and returned with updated, more specific queries to the AI.

This iterative approach allowed us to continuously improve the optimization process, adapting it to our project's specifics. With each cycle, we learned to better formulate queries for GPT-4, providing more context and specificity, which allowed us to receive increasingly relevant recommendations.

Practical Examples of Prompts for GPT-4 That Effectively Analyze Module Structures

The quality of results you get from GPT-4 directly depends on the quality of your prompts. Over several months of work, we developed a set of prompts that demonstrate the best results when analyzing Drupal code.

Here's an example of a prompt we used to identify deprecated functions:

This simple prompt allowed us to identify dozens of outdated functions that could cause problems during future Drupal updates. GPT-4 not only found issues but also suggested current alternatives, which significantly accelerated the refactoring process.

For optimizing the performance of custom modules, we used a more detailed prompt:

We once used this prompt to analyze a module responsible for report generation. The module worked acceptably with small amounts of data, but when the load increased, the response time grew to unacceptable values. GPT-4 discovered that the module executed a separate SQL query for each user, instead of retrieving all necessary data in a single query. After implementing the suggested changes, the report generation time decreased from 8 seconds to less than 1 second!

The prompt for migrating hooks to an object-oriented approach proved especially valuable:

"It was truly magical," recalls one of our team's developers. "The code we got after transformation was so clean and well-structured that it seemed like it was written by an experienced Drupal developer, not AI. And most importantly—it worked on the first try!"

Prompts for security auditing and optimizing themes and templates were equally important. GPT-4 helped us identify several potential XSS vulnerabilities and significantly optimize Twig template files, reducing code duplication and improving their readability.

Limitations and Pitfalls of Automated Solutions

Of course, the AI approach is not without its drawbacks and limitations. We quickly realized that blindly trusting all of GPT-4's recommendations wasn't advisable.

One of the biggest challenges was that GPT-4 analyzes code in isolation from the context of the entire project. It might suggest a technically correct solution that, however, would conflict with other parts of the system. This is exactly what happened when AI suggested changing the structure of a database table without considering that this table was used by other modules.

"The biggest mistake would be to view GPT-4 as a replacement for an experienced developer," explains our project manager. "It's a powerful tool, but it should work under human supervision, someone who understands the overall project architecture and business requirements."

Another limitation is the inability to run code to test recommendations. GPT-4 cannot verify whether a proposed solution will work in a real environment. Therefore, each recommendation requires careful testing before implementation.

We also encountered situations where AI proposed overly complex solutions for simple problems—a phenomenon we called "over-optimization." For example, for a simple news display module, GPT-4 suggested a complex caching system using Redis, although standard Drupal caching mechanisms would have been more than sufficient in this case.

It's also worth remembering the ethical and business aspects. Transferring commercial code to an external service may raise questions about confidentiality and intellectual property. And excessive reliance on AI recommendations could lead to a decline in technical expertise within the team.

Methodology for Verifying AI Recommendations Before Implementation

Recognizing AI's limitations, we developed a comprehensive methodology for verifying and evaluating recommendations before implementing them.

Each GPT-4 recommendation underwent technical review by experienced Drupal developers. We checked whether the proposed solution met Drupal coding standards and didn't conflict with existing functionality.

For critical changes, we wrote automated tests before implementation, which allowed us to quickly identify potential issues. Performance testing was also an important component: we conducted benchmarking before and after implementing changes to objectively assess their effectiveness.

"The iterative approach proved best," shares our QA team leader. "We implemented changes in small, manageable groups, making it easy to identify the source of problems if they arose."

Documentation was another important aspect. Each change was thoroughly documented with an explanation of why it was implemented and what results we expected. This not only helped other team members understand the logic behind the changes but also created a valuable knowledge base for future projects.

We paid special attention to establishing clear, measurable success criteria for each optimization. This allowed us to objectively evaluate the effectiveness of changes and avoid "optimization for optimization's sake."

Case Study: How We Optimized a Project with a 10-Year History and Reduced Technical Debt

We'd like to share generalized experiences from several projects that demonstrate typical optimization scenarios. In one such project, we worked with a corporate portal for a European company. The site was launched on Drupal 7, later migrated to Drupal 8, and then to Drupal 9. Over the years, the portal accumulated dozens of custom modules created by different development teams.

The main problems were slow performance (time to first byte exceeded 2 seconds), regular 500 errors during peak loads, and difficulties implementing new functionality. The technical debt included outdated custom modules, inefficient database queries, lack of proper caching, and mixing presentation with business logic. The most concerning issue was future Drupal updates, which without prior optimization could become an extremely complex process.

During the first phase, we used GPT-4 to analyze individual modules to create a preliminary "technical debt map" with prioritized issues. It's important to note that AI analyzed only code fragments, while the overall architectural understanding remained with the developers. This preliminary analysis allowed us to identify potential performance bottlenecks and establish achievable success metrics: improved response time, reduced CPU usage, fewer errors under load, and simplified code base to meet modern Drupal standards, which will facilitate future updates.

The next eight weeks were dedicated to refactoring and optimization. GPT-4 proved particularly useful in optimizing database queries. It found inefficient queries and suggested alternatives using Entity API, adding appropriate indexes, and implementing caching strategies.

Here's an example of one of GPT-4's recommendations:

This simple change not only made the code more readable but also significantly improved query performance thanks to Entity API's built-in caching.

Another important direction was migration to a service architecture. We transformed procedural code into services, implemented Dependency Injection, and clearly separated business logic from presentation.

We also paid considerable attention to frontend optimization: we optimized CSS/JS loading, implemented lazy loading for images, and refactored Twig templates.

The final phase included comprehensive testing and gradual deployment of changes. We conducted A/B performance testing, load testing simulating 10,000 simultaneous users, and regression testing of all functionality to ensure that optimization didn't break existing features.

The results were very positive. We achieved a reduction in time to first byte from 2.1 to 1.2 seconds (a 43% improvement), decreased CPU usage by approximately 25%, and significantly reduced the number of 500 errors under load. The code base became about 20% more compact, and its overall complexity noticeably decreased.

From a business perspective, this led to a tangible reduction in hosting costs, shorter development time for new features, and increased user satisfaction, reflected in positive feedback trends.

"We expected improvements, but not of this scale," recalls the company's CEO. "The portal doesn't just work faster—it's more reliable, and the development team can now implement new features much more quickly. This has allowed us to be more flexible in responding to business needs."

Technically, the site became better prepared for future Drupal updates, the number of errors in logs decreased, and the code became better tested and documented.

Conclusions and Recommendations

Our experience using GPT-4 for Drupal code optimization has shown that AI can be a powerful tool in the hands of experienced developers. However, it's important to understand that it's a tool, not a replacement for human expertise.

The key to success is a balanced approach that combines AI analysis with the experience of Drupal developers. GPT-4 excels at routine tasks such as finding deprecated functions or optimizing queries, but strategic decisions about project architecture still require human understanding of the business context.

A systematic process with a clear methodology for evaluating and implementing recommendations is essential for effective AI use. Without such a process, there's a risk of implementing changes that may have unforeseen consequences.

An iterative, recursive approach with constant feedback yields the best results. With each iteration, developers learn to provide more context to the AI and receive more relevant recommendations.

It's important to view optimization not as a one-time event but as part of a long-term project maintenance strategy. Technical debt tends to accumulate, so regular auditing and optimization are necessary to maintain project health in the long term.

If you're a site owner or represent a digital agency and are considering optimizing your Drupal project, here are a few recommendations:

Start with an audit. Use GPT-4 to create a detailed map of technical debt, which will help determine optimization priorities.

Establish clear success metrics. Define measurable indicators that will allow you to objectively evaluate optimization effectiveness.

Invest in team training. Ensure your team has the knowledge and tools to effectively evaluate AI recommendations.

Find a balance between speed and quality. Don't sacrifice quality for quick implementation of changes—this may lead to new problems in the future.

Document the process. Create a knowledge base of optimization patterns specific to your project, which will become a valuable resource for future updates and extensions.

Try the AI Approach for Optimizing Your Drupal Project

Don't let technical debt slow down your project's development. Artificial intelligence technologies can significantly accelerate the process of identifying and solving problems in Drupal code.

Start small: choose one module or component and try to analyze it using AI. Consult with Drupal development specialists who already have experience using AI for code optimization and can advise you. Or explore this approach yourself, using the prompts and methodology suggested in this article.

Combining human expertise with artificial intelligence capabilities is a powerful tool that will help maintain your Drupal project in excellent condition for years to come.