Notes from the context layer.
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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End-to-End System Evaluation: The Stress Test of GraphRAG
Individual layers may pass, but systems often fail at the seams. This blog details how to conduct holistic 'System-in-the-Loop' tests, measuring how retrieval noise compounds into generation errors across 25+ repositories. We provide a blueprint for evaluating the full journey from a vague natural language query to a multi-repo pull request.
Evaluating Generation and Grounding in Multi-Repo Systems
Retrieving nodes is only half the battle; the LLM must synthesize code that adheres to cross-repo constraints. This post explores measuring faithfulness, checking execution-level correctness against internal SDKs, and using LLM-as-a-Judge to verify that generated code respects the security and type contracts of separate repositories.
Evaluating Retrieval Quality in Cross-Repository GraphRAG Systems
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.
Building Your Evaluation Dataset from Organizational Repositories
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.
Designing a Three-Layer Evaluation Framework for Cross-Repository GraphRAG
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.
Mapping Your Organizational Codebase for Evaluation
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.
Why Standard Coding AI Benchmarks Fail for Cross-Repository Systems
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.