Beyond basic RAG: building a governed evidence engine
Exact terms, semantic meaning, graph relationships, and corrective retrieval solve different failure modes. The hard part is governing their combination.
CogLake Engineering · 11 August 2026 · 9 minOne retrieval method is not enough
Dense retrieval is strong at meaning but can miss identifiers. Lexical retrieval handles exact language. Graph expansion connects evidence that never appears in one chunk. Metadata narrows the problem before ranking begins.
Fusion needs policy
Combining candidate lists is only useful when the same identity and source permissions remain attached. CogLake verifies candidates before and after ranking so relevance cannot become an access-control bypass.
Measure the whole path
Useful benchmarks include retrieval quality, ACL isolation, original-file consistency, incremental update behavior, latency, and failure recovery. A single relevance score cannot represent production readiness.