Symbols Not Chunks: 3.9x Less Tokens

AST-Based Retrieval Cuts LLM Code Context 1.6 - 3.9x vs. LangChain RAG on Real Codebases J. Gravelle March 2026 Abstract Large language models (LLMs) consume tokens proportionally to the context they receive. When applied to code understanding tasks, the dominant retrieval strategy --- chunk-based Retrieval-Augmented Generation (RAG) using vector embeddings --- injects substantial irrelevant context, wastes tokens, and frequently delivers fragments that split functions mid-definition. This paper presents an alternative: AST-based symbol retrieval, which uses tree-sitter parsing to extract complete syntactic units (functions, classes, methods) and serves them via deterministic lookup. We benchmark both approaches on three open-source web frameworks (Express.js, FastAPI, Gin) totaling 1,214 files and 1,024,421 baseline tokens. In head-to-head comparison against a naive fixed-chunk RAG pipeline (LangChain + FAISS + MiniLM-L6-v2), AST retrieval uses 1.6--3.9x fewer tokens per query on ever