#Engineering Culture
Articles about Engineering Culture — exploring patterns, best practices, and real-world implementations in production systems.
3 posts tagged with engineering culture. ← All posts
The series finale — where LLM reviewers genuinely help on every pull request, where they quietly fail, and how to build a human-plus-AI workflow that speeds review up without letting judgment or accountability leak away.
The capstone: using AI/LLM reviewers well and where humans stay essential — AI as a tireless first pass that clears the noise, its limits (design judgment, contextual security, accountability, hallucinations), and a healthy human+AI division of labor.
How to run code review across a whole team or organization without turning it into a bottleneck: treat review latency as a first-class metric, distribute the load, let automation handle the toil, and measure the things that actually predict quality.
Making review work across a team without becoming a bottleneck: review velocity as a first-class metric, ownership and routing (CODEOWNERS), automating toil so humans do judgment, healthy SLAs, and measuring the right things.
The first post in a practical series on code review — what the practice is actually for, what it is not, and why treating it as a collaboration rather than a gate is what makes it worth the time it costs.
The opener to a code-review series: what review is actually for — catching defects early, better design, knowledge sharing, collective ownership — and what it isn't (gatekeeping, style bikeshedding), plus the healthy team norms that make it work.
All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.