As a tech lead, I recently tracked my team's time and found we spent an average of 12.5 hours per developer per week on PR reviews(include discussion of PR review results) - more than half of our coding time! This led me to question our approach to code reviews.<p>Key challenges we usually face:<p>- Are we wasting senior developers' time? How do we balance thorough reviews with efficient use of expertise?<p>- Ensuring consistent review quality across large teams and time zones<p>- Conducting comprehensive reviews in small teams without slowing development<p>- Focusing on the right aspects: architecture, logic, or style<p>- Using PR reviews for knowledge sharing and mentorship effectively<p>--------------------------------<p>I've been experimenting with AI-assisted PR reviews using Graph RAG for context understanding. The idea was to build a knowledge graph of our codebase that could make LLM understand Code relationships and dependencies. Early results show promise in reducing initial review time and catching consistent patterns, but it raises new questions about the balance between AI assistance and human expertise.<p>I'd love to hear your thoughts:<p><pre><code> 1. What's your team's current review process? What works well, and what doesn't?
2. How do you measure the effectiveness of your review process?
3. What kind of support do you think an ideal AI PR review tool should provide? Comprehensive analysis, focused insights, or specialized in certain aspects?
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If you're curious about AI-Powered PR review tool, I've published one called LlamaPReview that we've been using. It's still in beta, but feel free to check it out: https://github.com/marketplace/llamapreview/<p>Let's discuss how we can make code reviews more effective, efficient, and valuable for our teams!