Article : Preventing AI from Reinventing the Wheel: Teaching Tools to Recognize Existing Solutions

One of the most exciting capabilities of AI coding tools like ChatGPT, GitHub Copilot, and others is their ability to quickly generate solutions to problems.

However, a common issue developers face when using these tools is that the AI sometimes “reinvents the wheel” by generating functionality that already exists in established libraries or frameworks. This redundancy can lead to inefficiencies in the development process and, in some cases, introduce unnecessary complexity or even bugs.

In this article, we’ll explore strategies to help guide AI tools towards integrating existing solutions and libraries, rather than creating functionality from scratch when a more efficient, pre-existing solution is available.

1. Why Does AI “Reinvent the Wheel”?

AI tools are designed to respond to the prompts they receive by analyzing patterns from vast datasets, including open-source repositories, documentation, and other resources. However, the tools lack inherent awareness of the specific context of your project unless you provide it clearly.

This lack of awareness can lead the AI to suggest or generate solutions that may be correct in isolation but are not the most efficient approach in the context of your existing project. Often, it does not “know” that a specific function, class, or library is already available, leading it to generate redundant code.

For example, if you ask the AI to implement a sorting algorithm, it might generate a custom function when your project could easily make use of built-in functions like sorted() in Python or Array.sort() in JavaScript.

2. Strategies for Guiding AI Toward Existing Solutions

2.1 Provide Context in Your Prompts

A key factor in ensuring AI generates solutions that integrate with existing code or libraries is providing enough context in your prompt. By clearly describing the environment, libraries, and frameworks you’re using, the AI is more likely to suggest solutions that align with the tools you’re already working with.

• Example: Instead of asking, “How do I handle user input validation in Python?” ask, “How can I validate user inputs in Python using the validators library?” or “How can I implement input validation using Flask-WTF?”

By specifying the library or framework you’re using, the AI can focus on providing solutions that utilize those tools, rather than suggesting something from scratch.

2.2 Encourage AI to Use Existing Libraries

One effective way to prevent AI from reinventing the wheel is to explicitly ask the AI to use existing libraries for common tasks. Many programming languages and frameworks come with robust libraries for standard operations such as data validation, sorting, logging, or even handling HTTP requests.

• Example: If you’re working with a Python project, you could specify in your prompt: “Use the requests library to make a GET request to the API,” or “Please use pandas to handle the CSV file operations.”

By instructing the AI to prioritize specific libraries, you help guide it toward more efficient solutions that leverage well-tested code, reducing the need for custom, potentially error-prone implementations.

2.3 Highlight Existing Functions or Modules in Your Codebase

If you’re working with a larger codebase, it’s possible that similar functionality has already been implemented. In such cases, encourage the AI to check existing code or even review your project’s documentation.

• Example: “We already have a function validate_email() in utils.py. Please use this function to validate email inputs.”

This helps prevent redundant code and encourages the AI to integrate solutions that are already in place.

2.4 Ask for Refactoring Instead of Rewriting

If you’re asking the AI to handle a specific task or functionality, consider asking it to refactor existing code instead of starting from scratch. This approach helps prevent the AI from generating new code unnecessarily.

• Example: “Refactor the existing login authentication function to add error handling,” instead of “Create a new login authentication function.”

By framing your request as refactoring, the AI will focus on improving the current code rather than generating a new implementation.

2.5 Encourage Reusability and Modularity

When crafting prompts, you can also guide the AI to think about modularity and reusability. If a solution exists in one part of your codebase, prompt the AI to use it again instead of creating a new, isolated implementation.

• Example: “Use the existing send_email() function in the email_utils module to send an email confirmation,” rather than asking it to write a new function to send emails from scratch.

This reinforces the importance of using pre-existing solutions and helps maintain a cleaner, more maintainable codebase.

2.6 Specify the Need for Efficiency

If you want to avoid redundant code, you can instruct the AI to optimize for efficiency by recommending or integrating already available, more efficient solutions.

• Example: “Please use the built-in collections.Counter in Python for counting occurrences of items in the list, instead of writing a custom function.”

This reinforces the need for AI to prioritize built-in or library solutions that are optimized and tested for performance.

3. Integrating AI with Your Existing Tooling and Workflow

3.1 Use Linters and Static Analysis Tools

Once the AI generates code, you can set up linters and static analysis tools to ensure that the generated code aligns with your project’s standards, including avoiding redundant or inefficient code. Some linters can even identify areas where existing libraries or functions could be used instead of reinventing the wheel.

• Action: Integrate tools like pylint, ESLint, or SonarQube to scan the AI-generated code and flag redundant implementations or suggest the use of built-in functions.

3.2 Create a “Common Patterns” Repository

If your team works with certain libraries or frequently reuses certain patterns (e.g., database connection handling, authentication), consider creating a “common patterns” repository. This repository can house snippets of reusable code that both the AI and your team can reference.

• Action: Create a shared library or internal documentation with standard patterns for common tasks, and ensure the AI is aware of these resources during the development process.

3.3 Review AI-Generated Code for Redundancies

As part of your code review process, ensure that generated code does not duplicate existing solutions. Encourage reviewers to check that any AI-suggested code integrates with existing libraries or functions rather than implementing a new solution unnecessarily.

• Action: During code reviews, check for duplicated functionality, encourage the use of standard libraries, and avoid custom code where a library function or pre-existing solution is available.

4. Conclusion

While AI tools have the potential to significantly speed up development, they can also lead to redundant or inefficient code if not properly guided. By providing detailed context in your prompts, encouraging the use of existing libraries, and focusing on reusability and modularity, developers can ensure that AI-generated code integrates seamlessly with their current projects and avoids reinventing the wheel.

Ultimately, AI should be seen as a tool for enhancing developer productivity, not replacing existing best practices.

By guiding the AI toward leveraging established solutions and libraries, you can ensure that your projects remain efficient, maintainable, and free from unnecessary code duplication.