Developers using AI to generate pull requests face rejection and confusion due to code quality and context comprehension issues.

AI-generated pull requests often fail code review because LLMs lack understanding of project-specific conventions and architectural patterns

Common rejection reasons include poor code quality, missing documentation, and failure to follow established coding standards

AI tools struggle with context awareness, missing important details about existing codebase dependencies and design decisions

Developers should use AI as a starting point rather than a complete solution, requiring manual review and refinement before submission