Using ChatGPT directly for real-time linting rules in high-load systems is generally not a recommended best practice. This is primarily due to latency concerns, the non-deterministic nature of LLM outputs, and the significant computational overhead and cost associated with API calls for every code change. Instead, best practices leverage ChatGPT offline for tasks like generating complex custom linting rules or refining existing ones that might be challenging to articulate programmatically. It can also serve as a powerful tool for explaining linting violations to developers, providing context and suggesting specific, compliant code alternatives, thereby enhancing developer understanding and productivity. Furthermore, ChatGPT can assist in creating comprehensive documentation for linting standards or even generating synthetic code examples to train specialized, lightweight rule-checking models that are deployed locally for faster, more predictable results. Crucially, its application should remain asynchronous and outside the critical path of the build and deployment pipeline to maintain system performance and reliability. More details: https://track.colincowie.com/c/?url=https://abcname.com.ua/