for anyone doing serious code reviews, this lines up with how TuringMind AI has evolved: RAG is nice for “what looks like this code?”, but impact-aware review is almost never a pure similarity problem. What actually matters in review are things like “what configs/migrations/env vars does this touch?”, “what else depends on this function?”, and “are we creating a new unwanted coupling?”, which are fundamentally dependency-graph questions. That’s why TuringMind leans on a multi-language AST + dependency graph for review and then uses embeddings only where they shine (semantic search, exploration, explanations), rather than forcing a vector database to answer impact questions it’s structurally bad at.