Benchmarks, retrieval architecture and the engineering behind indexing a codebase once and serving it to every tool that asks.
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A practical guide to measuring retrieval quality in GraphRAG systems operating across multiple repositories. Covers gold-standard design, graded relevance metrics, cross-repository precision, graph traversal evaluation, and version coherence to ensure correct multi-repo retrieval.
Before creating evaluation datasets for a GraphRAG system, you must understand your codebase topology. This post walks through building repository dependency graphs, classifying repos by role, mining real developer questions, and identifying high-priority code regions that stress cross-repository retrieval.
A comprehensive evaluation architecture for GraphRAG systems operating across multiple repositories. This post introduces the retrieval → reasoning → generation framework with specific metrics, target thresholds, and implementation code for each layer.
Before creating evaluation datasets for a GraphRAG system, you must understand your codebase topology. This post walks through building repository dependency graphs, classifying repos by role, mining real developer questions, and identifying high-priority code regions that stress cross-repository retrieval.
Existing benchmarks like HumanEval, MBPP, and SWE-Bench assume single-file, isolated context and cannot evaluate GraphRAG systems that reason across tens of thousands of files, multiple repositories, and evolving services. This post explains the unique failure modes in cross-repository retrieval and what metrics actually matter.
Vector search finds relevant code but misses the blast radius. Learn how combining lightweight code graphs with RAG creates cross-repository context that makes code changes across 50+ repositories predictable—without heavy graph infrastructure.
Move beyond keyword matching with semantic code search. Learn how embeddings, function-level understanding, and knowledge graphs transform code discovery—plus why citations matter for enterprise teams who can't afford hallucinated answers.
