How to Use Code-Graph-RAG Without Mistaking a Rich Graph for Certainty

Code-Graph-RAG answers the question raised at the start of this series in a specific way: it makes a repository more understandable by building a shared structural model and then attaching different kinds of evidence to that model. The final judgment is not whether the project can make code sound intelligible. It is when a reader can responsibly trust a graph-backed conclusion, when they should add another evidence layer, and when they should stop and narrow the claim. That judgment depends on the connection among representation, retrieval, action, freshness, coverage, and security.

One graph, several kinds of evidence

The structural graph is the stable center of the design. Tree-sitter-based analysis turns source into nodes and relationships that preserve hierarchy, definitions, imports, calls, implementations, references, and other project-specific structure. Memgraph stores that model so the same repository can be queried repeatedly. A language model can help translate an open-ended question into a graph query, but the model does not become the graph’s source of truth. Deterministic tools can inspect known names and locations without adding a second interpretation step.

This arrangement gives Code-Graph-RAG a useful separation of responsibilities. The parser answers what the source can be represented as. The graph answers how represented entities relate. Retrieval surfaces choose how a person or program asks about those relationships. Verification checks whether the selected relationship supports the sentence a reader wants to write. When a change follows, the editing layer preserves and validates the source, while re-ingestion keeps the derived graph aligned with it.

The layers are complementary because they observe different things. Structural evidence can show that a module imports another, that a function has recorded callers, or that a type implements an interface. Semantic search can help locate a function whose name says little about its purpose. Runtime tracing can add a callback, registry route, or concrete dispatch that static analysis did not resolve. Data-flow analysis can connect a modeled resource source to a modeled sink. None of these layers is a replacement for the others.

A reader can treat the system as an evidence ladder. Start with the narrowest structural fact that could answer the question. If the symbol or location is known, use a deterministic graph tool. If the concern is broad and the graph vocabulary is not known, use a model-mediated query, then inspect the returned relationship and source. If the problem is discovery by behavior, use semantic search to find candidates, then confirm their definitions and relationships. If the uncertainty concerns runtime dispatch, add a trace for a selected workload. If it concerns a value crossing an external boundary, use the opt-in data-flow model and check its coverage.

This is more disciplined than asking one assistant to “understand the codebase” in the abstract. It also avoids making the workflow needlessly rigid. A semantic search result can be the starting point for an exact caller query. A model-generated architecture query can identify symbols whose callers and importers are then examined with deterministic tools. A runtime trace can confirm a static edge and expose a missed edge, while the structural graph remains the place where both are stored and compared. The graph is the common substrate; the evidence path changes with the claim.

The project’s applications make the distinction concrete. Dead-code analysis walks static CALLS and REFERENCES from roots such as public symbols, tests, decorated handlers, and user-specified entry points. The result is a candidate list for review. Duplicate detection compares syntax-tree skeletons and branch fingerprints, so it finds exact, renamed, and lightly edited copies without claiming that two functions are behaviorally equivalent. Structural search and replacement work on AST patterns rather than text patterns.