A practical walk through the RAG pipeline: chunking, reranking, embeddings, and where the vectors live.
Chunking quietly decides how good your RAG system can ever be. Here is how the main strategies compare and a simple way to pick one.
Rerankers re-score your top candidates with a model that actually reads the query and the document together. Here is how they work and when they pay off.
Embeddings turn text into vectors so meaning becomes geometry. Here is the mental model, the gotchas, and how to choose a model in 2026.
A practical, opinionated comparison of the four vector databases people actually argue about, with a clear default and the signals that mean you have outgrown it.