Posts tagged "code embeddings"

AST vs Vector vs Graph vs IR: Four Ways to Give an LLM Your Codebase

Every tool that feeds your codebase to an AI coding agent picks one of four representations. AST parsing, vector embeddings, code graphs, or a verifiable intermediate representation. Here is what each one keeps, what each one throws away, and why the first three are features of the fourth.

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.

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.

Semantic Code Search Explained With Examples From Real Codebases

Move beyond keyword matching with semantic code search. Learn how embeddings, function-level understanding, and knowledge graphs transform code discovery—plus why citations matter for enterprise teams who can't afford hallucinated answers.