vector search limitations

A Code Graph Disintegrates Your Code. A Verifiable IR Preserves It.

A code graph takes a whole, coherent program and shreds it into a bag of symbols and edges. It is genuinely useful, but it is a teardown, and the meaning never survives the pieces. A verifiable intermediate representation is the opposite move. It lifts your code into a higher layer that keeps the intent intact, then checks every change against it. Here is the difference between shredding and preserving, and why it decides everything downstream.

Why Vector Search and GraphRAG Both End in the Same Context Rot

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.

How We Hit 94% Accuracy at 20% of the Tokens Across 46 Repos

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.

What Verifiable Means for Code Context, and Why GraphRAG Can't Check Code Against Intent

Verifiable is the one word competitors cannot claim. A code graph extracts a shadow of your code and can never tell you whether the code still does what it is supposed to. A verifiable intermediate representation is a derived contract that every change gets checked against. Here is what verifiable actually means, why GraphRAG and vector search can only retrieve, and what continuous verification unlocks.

Code Graph RAG, Explained, and Why a Verifiable IR Is the Next Step Past It

Code graph RAG fixed the biggest flaw in vector search. It follows real edges instead of guessing at similarity. But a graph of symbols is still not a graph of meaning, and it cannot tell you whether a change still does what the system is supposed to do. Here is how code graph RAG actually works, where it stops, and why a verifiable intermediate representation is the layer above it.

Semantic Code Search Is a Retrieval Problem. Context Is a Representation Problem.

Semantic code search keeps getting better at finding the right code, and it keeps disappointing teams who expected better answers from their AI tools. The reason is that those are two different problems. Finding code is retrieval. Understanding code is representation. Better retrieval over a poor representation has a ceiling, and here is why a verifiable code IR raises it.

Why Vector Search, AST Parsers, and Raw LLMs All Fail at Code Intelligence — And What Actually Works

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.