Engineering deep-dives, benchmarks and customer stories on persistent code context, knowledge graphs, and the AI coding tools we work with daily.
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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.
Modern AI models advertise million-token context windows like they're breakthrough features. But research shows performance collapses as context grows. Here's why curated context and precise retrieval beat raw token capacity—and how we've already solved it.
