Tim Ainge starts with schemas and ontologies to make graph extraction consistent and queryable. Recipe examples show why generic triples can be less useful than explicit entity types, standardized units and carefully defined relationships. Embeddings can help reconcile duplicate entities without requiring every possible spelling in advance.
Tim Ainge demonstrates personalized PageRank for discovering related information and shortest paths for supplying code context between known symbols. He reports fewer code-search tool calls in one .NET evaluation, not a universal performance guarantee. His examples connect graph structure with retrieval rather than presenting GraphRAG as a cure-all.
Tim Ainge also uses subgraph matching to find design patterns from relationships between classes and interfaces. The practical lesson is to choose an algorithm for the question being asked, combining graph queries with AI when connected information provides value that isolated text similarity cannot.
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