5 posts carrying this tag.
We measured how coding agents search with grep on Home Assistant and Saleor: about 150 model runs and 13 million tokens. Grep finds the files, then buries them in noise, and it can't follow inheritance or code that never says the name. A bigger model doesn't fix it.
Grep finds 87% of the files a question needs, then buries them in noise. We put TypeSafe's Jev classifier behind grep on 5,075 candidate files from Saleor, Kubernetes, Istio, Argo CD, cert-manager and etcd. It found more of the right files than Opus 5.5 at about 1% of the cost.
Vector search and GraphRAG look like opposites, but they share the same final move. Parse the code, retrieve some of it, rerank it, and stuff it into the context window. That last step is where both of them rot, because a model that is handed a pile of chunks degrades the same way no matter how the pile was chosen. Here is why the retrieval layer is not the cure for context rot, and what is.
The claim that a verifiable IR beats vector search and GraphRAG only matters if the numbers hold up. So here is the run. 46 Kubernetes-ecosystem repositories, 150,000 files, 8 GB of code, measured end to end. What we tested, how we scored it, and why feeding the model 22x more context made it cheaper and more accurate at the same time.
The objection to compiling a codebase into a verifiable IR is always cost. It sounds like Opus pricing across every file. It isn't. At open-source-model rates it runs about $13.30 per 1,000 files, you pay it once, and per-file diffing means you only ever re-pay for what changed. Here is the real economics of the verifiable context layer, and why the expensive thing is not building the IR but living without one.
