neuralmind

NeuralMind vs. Generic RAG over a codebase

What “generic RAG” means here

The standard recipe: chunk files by lines or tokens, embed them, store in a vector DB, retrieve top-k by cosine similarity, concatenate into the prompt. Works fine for documentation and long-form text.

Why code needs more

Code is not prose. It has structure (call graphs, imports, class hierarchies) that text chunking destroys. NeuralMind keeps that structure and uses it.

Dimension	Generic RAG	NeuralMind
Unit of retrieval	Text chunk (e.g. 500 chars)	Graph node (function, class) with metadata
Context	Flat list of chunks	Progressive: identity → summary → clusters → hits
Call graph	Lost at chunking	Preserved, used in skeletons
Community/cluster awareness	None	First-class (top clusters by relevance in L2)
Cross-file edges	Not encoded	Explicit (imports_from, shares_data_with)
Token budget	You enforce it	Built-in, reported per query
Consumption-side savings	None	Read/Bash/Grep PostToolUse compression

When to pick which

• Pick generic RAG if you are indexing docs, tickets, or mixed content where structure doesn’t matter.
• Pick NeuralMind for code. The graph-aware layers and skeleton output capture the structural context that generic chunking throws away — and you don’t pay for the throwaway tokens.

If you already have a generic RAG pipeline and only want the compression half, NeuralMind’s PostToolUse hooks can run standalone without the retrieval layer.

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