4 posts carrying this tag.
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.
Your team uses Claude Code, Cursor, Copilot, and Windsurf, and each one rebuilds an understanding of your codebase from scratch and shares nothing with the others. MCP is the standard that lets you stop doing that. Compile your repos once into a verifiable IR and serve it to every tool over one MCP endpoint. Here is why the context layer belongs outside the tool, and what changes when it does.
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.
Vector embeddings treat code like english prose. AST parsers see structure but not meaning. Raw LLMs forget everything every session. Here is why the LLM compiler pattern with a persistent semantic graph is the only approach that actually works for cross-repository code intelligence, and why open source models at $7 per 1000 files make it practical today.
