2010YOUY01 opened a new issue, #25317:
URL: https://github.com/apache/datafusion/issues/25317

   ### Is your feature request related to a problem or challenge?
   
   LLM-generated PR reviews are becoming more common in DataFusion, and they're 
often helpful. But I've noticed some hidden issues with them, and I'd like to 
start a discussion to exchange opinions.
   
   Once we've reached some agreement, we can document it as an AI review 
policy, similar to 
https://datafusion.apache.org/contributor-guide/index.html#ai-assisted-contributions.
   
   ### Issue
   
   LLM-generated reviews are sometimes hard to parse. Human-written review 
feedback is usually easy to understand: it gets to the point in one or two 
sentences. AI reviews (say, today's Codex/Claude with the best models in 
default mode) can be hard to understand: they throw a verbose amount of detail 
at you, and you have to spend time reconstructing the idea behind it.
   
   This consumes the contributor's time on interpretation. One potential 
consequence is that it encourages contributors to let AI address the review 
feedback entirely. Such a loop would degrade the codebase quality very quickly, 
since today's LLM agents still can't handle medium-complexity tasks in 
DataFusion well.
   
   ### Proposed guidelines:
   ```text
   1. AI reviews are always encouraged, but the contributor can address them 
selectively and skip the ones that are hard to parse or overly verbose. 
     (I believe some of them are easy to understand directly; only some are 
not.)
   3. Reviewers are encouraged to do the interpretive labor: first understand 
the LLM-generated review, then express it in an understandable way.```
   
   ### Describe the solution you'd like
   
   _No response_
   
   ### Describe alternatives you've considered
   
   _No response_
   
   ### Additional context
   
   _No response_


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