RAG Retrieval: Chunking, Embeddings, Reranking, and an Eval
This series covers building a RAG pipeline to answer questions about the Anthropic documentation. A RAG agent answers questions by first searching a private knowledge base, then passing the relevant excerpts to an LLM as context — the model reads the actual source material before it responds, rather than guessing from training data. Here the focus is the retrieval layer: how to chunk text, embed it, retrieve it, and measure whether retrieval is actually working. ...