Building a RAG Pipeline for Semantic Code Search
This article explores the technical challenges of building a Retrieval-Augmented Generation (RAG) pipeline for semantic code search. It emphasizes the need for moving beyond keyword-based search to help AI agents navigate complex codebases more efficiently.
Why it matters
Improving how AI agents interact with large code repositories is critical for the future of automated software development.
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Part 1: Parsing, chunking, and vectorization
Some time ago, we set out to build the best semantic code search platform we could: a RAG pipeline that gives LLM agents precise, citable evidence from real repositories instead of whatever grep happens to surface. The eventual solution was Air Context . We got it working, we got it into production, and we collected a lot of scar tissue along the way. In this series of posts, we’ll share the parts we wish someone had told us on day one.
Coding agents are undoubtedly the biggest technology leap for software development of our decade. Agents and frontier models are proving their aptitude in the face of seemingly insurmountable code complexity to produce ostensibly reliable code.
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