When you face a pull request with thousands of changed lines, don’t slog through each hunk by hand. Point an LLM at the diff instead. Let it flag syntax errors, trivial style violations and common vulnerability patterns. It handles those in seconds—no human reviewer can match that speed on a giant patch. Your real value comes from what the AI doesn’t know. Feed it context from your recent discussions and codebase conventions. Remind it that service_A is about to be deprecated. Tell it why we never attach new fields to the root object. Highlight the specific metric conventions we follow. With those prompts, the AI can fold in your institutional knowledge and spot design choices that no generic model would catch on its own. This approach breaks down when every line demands absolute precision. Embedded systems, firmware or safety-critical modules still need humans poring over every bit. But for day-to-day backend, web or mobile code, pairing your domain expertise with an LLM shrinks review time and lets you focus on architecture, edge cases and cross-team dependencies.