2010YOUY01 commented on issue #18095:
URL: https://github.com/apache/datafusion/issues/18095#issuecomment-3419224247

   > My two cents as a contributor: Let's remember that there's no guarantee 
that people will tell the truth on the internet. I don't think this will help 
much with the most problematic PRs where all the code is fully AI-generated and 
the PR feedback is handled by AI, too. I think the same people who author these 
PRs will also **lie** about their use GenAI use.
   
   I think people are not telling the truth because of misunderstandings rather 
than malice. We should better explain why large AI-generated PRs without 
understanding are not helpful.
   
   Here are my opinions. If we can reach agreements, they can be added to the 
contributor guide:
   
   **If the PR author only understands a small part of the implementation 
details, such PR should be rejected**
   The PR and code review system exists for two major reasons:
   - Get the job done, obviously.
   - Share knowledge about the codebase between author and reviewers, which is 
a long-term investment in the project. For this reason, even if someone 
familiar with the codebase can finish a task quickly, it’s still worth helping 
a new contributor work on it even if it takes longer.
   
   This doesn’t work for fully AI-generated PRs without understanding, because 
reviewers are mostly prompting through a pass-through human proxy. They can 
solve the problem faster by using AI directly, and there is almost no knowledge 
sharing since the PR author doesn’t want to understand the solution.
   
   A better way to help is to write a better issue to make it easier for others 
to contribute, instead of generating a full solution.
   
   **If the author knows most of the solution but is missing some corners, such 
PRs are welcome**
   One case is implementing a large trait, where the author knows the 
high-level idea but isn’t sure if a certain interface’s default implementation 
is okay to use. This might be due to under-documentation of the API.
   They're encouraged to point out exactly which part of the PR they don’t 
fully understand, the review can focus on that and potentially improve the 
documentation.
   


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