The Compare the Market (Meerkat Careers, UK) engineering team published, on March 6, 2026, an empirical evaluation of four context-retrieval approaches for AI code review: Baseline (no additional context), RAG (vector search via embeddings), GKG (GitLab Knowledge Graph, an AST-based knowledge graph via Tree-sitter and the Kuzu graph database), and a GKG+RAG hybrid. The evaluation covers 79 real merge requests, measured via MLflow on Databricks.
The main finding is counterintuitive: RAG performs worse than the baseline on almost every metric, including inline comments coverage, summary coverage, and score accuracy. Adding context retrieved via vector similarity is not only useless but counterproductive for code review. Four causes are identified: noise (vector similarity retrieves code that "looks similar" without being relevant), false positives, the lack of understanding of cross-file relationships, and a distraction effect that misleads the model.
Conversely, GKG outperforms RAG by +21% in inline comments coverage (0.696 vs 0.577). The reason is structural: code review requires knowing who calls a function, what it calls, and how it fits into the architecture — information that the AST and the knowledge graph capture natively, but that semantic similarity cannot provide. GKG precisely identifies callers, understands function signatures, and traces code relationships.
The implementation is pragmatic: since GKG is still in beta and not yet natively integrated into GitLab CI/CD, the team built a Docker sidecar container that wraps the GKG binary, indexes the codebase on every MR pipeline, and exposes the tools via a local MCP server. The cost is 4× the baseline, but the improvements are measurable and justified. RAG costs 3× the baseline for worse results.
This study confirms a major 2026 trend: for code, structural approaches (AST, knowledge graphs, targeted grep) outperform vector-based approaches (semantic RAG). Code is not text — its informational value lies in its structural relationships, not in its lexical similarity. Strong convergence with Zhutov/QMD, Dropbox/Okumura ("the value comes from the systems surrounding the model"), and the Anthropic Data Science doctrine ("the bottleneck is structure, not access"). To be used as empirical reference for AI code-review architecture choices and as a counter-argument to RAG-by-default in the code domain.