How embedding models, chunking, retrieval modes, and query phrasing jointly determine Code-RAG quality.
Benchmark on Kafka tests 16 embedding models and 5 chunking strategies; model rankings shift with pipeline configuration. For code-RAG projects, model selection alone won't optimize search quality; chunking strategy and retrieval configuration must be tuned together.
When developers enter an unfamiliar project, they rarely search for a specific
file by name. They usually ask about system behavior: where incoming
connections are accepted, which component cleans logs, or how a request travels
between architectural layers.
Code-RAG tries to answer such questions through semantic search. It splits and
Introduction Here is a scenario many RAG builders know well, you wire up a pipeline, load...
RAG (Retrieval-Augmented Generation) is the foundation of knowledge-grounded AI. But most RAG...
If your RAG app sometimes answers from the wrong document even though the right one was in your...
RAG sounds complicated. It's not. But a lot of introductions to RAG make it sound more mysterious...
Why modern AI coding agents often use grep, file reads, symbols, and tests before reaching for vector RAG.
Build a RAG pipeline from scratch: chunking, embeddings, retrieval, reranking, grounded generation, and the production patterns…
