GETTING AI CODING ASSISTANTS RIGHT ON LARGE REPOS
Hybrid indexing, agentic loops, and model routing—not bigger context windows—are the real keys to making AI coding assistants reliable on large codebases. The ...
Hybrid indexing, agentic loops, and model routing—not bigger context windows—are the real keys to making AI coding assistants reliable on large codebases.
The Kilo Blog post argues that context window size is a red herring. Most tools fetch the wrong files, ignore dependency graphs, and reset state on every request. It proposes combining AST/code graphs with vector search to give assistants structural and semantic understanding.
It recommends agentic loops so models can plan, act, observe, and self-correct, plus routing work to the right model for each task. The post also offers evaluation guidance and purchase questions for leaders choosing tools. Use it to shape proofs of concept and your platform roadmap.
Better retrieval and code graph awareness reduce misleading suggestions and production risk in large systems.
It provides a concrete architecture and evaluation rubric to guide build-or-buy decisions.
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Measure retrieval quality by changing a shared type and verifying the assistant traces all dependent impacts across the graph.
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Evaluate agentic loops on a multi-file refactor, checking for self-correction and rollback when tests fail.
Legacy codebase integration strategies...
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Pilot a hybrid index over the monorepo and compare assistant accuracy versus your current tab-based context approach.
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Generate a code graph for legacy services and schemas to cut hallucinated references before wider rollout.
Fresh architecture paradigms...
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Design repo structure and module boundaries to maximize code graph clarity and fast indexing from day one.
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Adopt tools with native code graphs and model routing, and update the index as part of CI.