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Why Does ChatGPT Miss Unique Domain Logic In Code Reviews? Curious about why ChatGPT might not always catch the unique rules in your code reviews? In this video, we explore the reasons behind ChatGPT’s limitations when analyzing specialized codebases. We’ll explain how its understanding is based on general patterns rather than specific project details. You’ll learn why ChatGPT might miss custom design patterns, private APIs, or domain-specific logic that are vital to your project’s success.

We’ll discuss how ChatGPT’s limited view of your system prevents it from seeing how different parts of your code interact or how your architecture is built. You’ll discover why it can’t access version history or understand decisions made during development, which are often key to thorough code reviews. We also cover how ChatGPT’s knowledge is based on data up to a certain date, making it less effective with the latest frameworks, libraries, or security practices.

Additionally, we’ll highlight how ChatGPT doesn’t truly grasp the business rules or the purpose behind your code, which can lead to missed issues or false alarms. We compare it to static analysis tools that perform deep analysis, emphasizing the importance of combining AI assistance with human expertise and specialized tools.

Join us to learn how to maximize ChatGPT’s benefits while understanding its limitations. Subscribe for more tips on coding with AI tools and improving your development workflow!

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