The Importance of Training Data Availability: Why Chasing Trends in Frameworks Could Limit Your LLM Experience

As programming techniques evolve, the quality of a large language model’s (LLM) code generation is intimately tied to its training data. This correlation becomes glaringly obvious when comparing the results for well-established frameworks versus those that are relatively new or trending.

The Value of Open-Source Training Data

The availability of training data from open-source code repositories is crucial for LLMs to generate accurate and useful solutions. When an open-source project has been around for a while and is widely available on the web, the LLM can draw from a vast repository of examples to provide answers that align with recognized best practices. For instance, when working with Python or React, most major LLMs produce excellent code.

React, for example, has matured over many years and remains popular, resulting in a wealth of training data. Whether I'm asking an LLM to generate component-based solutions or optimize state management, the model almost always returns something useful. It understands the various patterns and practices because of the extensive training data available.

The Challenges of New Frameworks and Trends

On the other hand, new frameworks and trends pose a different challenge. With less training data available, even sophisticated models can struggle to deliver accurate results. Take HTMX or Svelte, for example—both are relatively new compared to React. In my experience, LLMs often falter when providing effective recommendations for these frameworks, primarily due to the limited number of public repositories and documentation available at the time of training.

This gap in training data becomes evident in the quality of the code outputs. If an LLM is forced to make suggestions for something new with limited resources, the responses are more likely to be inaccurate or outdated. When I tried generating code in Svelte, the results were inconsistent, often misinterpreting the framework's syntax or overlooking essential conventions. If I had relied solely on an LLM to guide my understanding, the solutions would have been unsatisfactory.

Frameworks and the Balkanization of Web Development

With the rapid proliferation of new frameworks, developers are increasingly finding themselves in specialized silos. Although each framework promises to streamline or improve development, this trend ultimately balkanizes web development, creating isolated groups of developers tied to specific ecosystems. This segmentation makes it harder to access accurate LLM guidance because models often lack sufficient training data for less popular frameworks.

Leveraging LLMs for Simpler Solutions

Instead of relying solely on newer frameworks, consider asking LLMs to convert specialized framework code into simpler syntax or directly to web standards. This approach can improve code clarity, maintainability, and flexibility across projects. For instance, React components can often be translated to plain JavaScript, resulting in concise, easier-to-understand solutions that don't require specialized libraries.

By allowing LLMs to help convert modern framework code back to web standards like HTML, CSS, and JavaScript, developers can avoid being pigeonholed into trendy tools and instead focus on universal best practices. This flexibility makes transitioning between projects or frameworks smoother while ensuring that your solutions are compatible across a wider range of environments.

Conclusion

While experimenting with the latest frameworks can be enticing, developers should be mindful of the limitations imposed by scarce training data. Without it, LLMs may struggle to offer valuable recommendations or accurate refactoring suggestions for emerging frameworks. Instead of embracing every new trend, consider using these models to translate specialized code into simpler forms that align with web standards, ensuring efficient collaboration and more comprehensive solutions.