5 posts carrying this tag.
GitHub Copilot moved to per-token billing and one developer's bill jumped from about $29 to a projected $750. Switching tools won't save you, because the same shift is happening everywhere. Here's why your bill exploded in plain English, the practical levers that cut it today, and how verifiable specs attack the cost at its root.
Per-token billing turned a flat $29 AI coding plan into a $750 surprise. The waste was always there: most of the bill is your AI re-reading code it already saw. Here's why it happens and how to cut it, with prompt caching, the right model, the right plan, and a lasting memory of your codebase.
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.
