Mastering LLM Code Quality: Strategies for Consistent, Maintainable Software

The rise of Large Language Models (LLMs) in software development has sparked a crucial conversation about "code quality." While these tools offer incredible speed, they also present unique challenges. Understanding what constitutes "bad quality code" and implementing strategic workflows can help developers harness LLMs effectively.

What Constitutes "Bad Quality" LLM Code?

Discussions often highlight several key indicators of low-quality LLM-generated code:

• Inconsistent Styles: LLMs can pull from diverse training data, resulting in a mix of coding styles within the same function or module (e.g., using both if [ ] and if [[ ]] in Bash, or mixing older and newer language features like Dart's get/set keywords). This lack of consistency makes code harder for humans to read and understand, akin to reviewing legacy code maintained by multiple developers with varying habits.
• Deprecated API Usage: LLMs may suggest or implement solutions using outdated APIs, leading to compatibility issues and future maintenance burdens.
• Convoluted or Implicit Logic: Rather than clear, declarative code, LLMs can sometimes produce overly complex or "spaghetti code" that achieves a goal but lacks elegance and simplicity, making debugging and future modifications challenging.

Strategies for High-Quality LLM-Assisted Development

Effectively leveraging LLMs while maintaining code quality requires a thoughtful approach:

Architect First, Delegate Implementation

A critical strategy is to separate architectural design from implementation details. Developers should define the overall structure—ensuring high cohesion, loose coupling, and adherence to principles like Service-Oriented Architecture (SOA)—and then use LLMs to implement individual functions or small, well-defined components. This ensures the foundational design integrity remains in human hands.

Treat LLM Output as "Sample Code"

Don't blindly accept LLM-generated code. Approach it as "sample code" or a first draft that requires thorough human review, modification, and often, significant refactoring. This critical evaluation is similar to adapting a solution found on external resources rather than deriving it from first principles.

Prioritize Consistency, Especially in Large Codebases

When integrating LLM-generated code into an existing project, consistency often trumps adopting newer language features. For a large codebase, maintaining a uniform style is paramount for readability and maintainability, unless the existing style is egregiously poor or "idiosyncratic." Developers should either correct the LLM to match the existing style or commit to a comprehensive refactor if the new style offers significant benefits.

Focus on Small, Highly Focused Tasks

The more constrained and specific the task given to an LLM, the better the quality of the output tends to be. Assigning LLMs to write small, focused functions or utilities, rather than large, complex features, leads to more readable and compact code that's easier to review and integrate.

Automate Testing and Iterate

To reduce the human-in-the-loop for basic correctness, consider setting up automated pipelines. This might involve using tools to write frontend integration tests, capture screenshots (e.g., with Playwright), and loop the LLM's generation until tests pass. While this can improve correctness per run, human code review remains essential for ensuring the quality of the working code.

Balancing Speed and Perfection (Especially for Startups)

For early-stage startups, the primary goal might be to achieve a Minimum Viable Product (MVP) rapidly. In such cases, the advice is to focus on getting the code to solve the problem and ship it. Perceived "sloppiness" can be addressed later, with fixes prioritized based on actual customer complaints and feedback, rather than delaying launch for aesthetic perfection.

The Enduring Role of Human Developers

Even with advanced LLM capabilities, the "taste" and experience of a human developer are irreplaceable. Reviewing code to identify convoluted logic, ensuring adherence to best practices, and maintaining architectural vision are skills that LLMs currently lack. Staying updated with best practices through reading open-source codebases and engaging with developer communities remains crucial, even as LLMs increasingly draw from the same data. While LLMs can save time and make coding "fun," uncritical acceptance of their output risks accumulating significant technical debt.