Retrieval-Augmented Generation (RAG) Tutorial: Chunking, Embeddings, and Grounded Answers

<p>A hands-on, math-first tutorial on Retrieval-Augmented Generation: ingest and clean real documents, design metadata schemas, compare chunking strategies, choose and normalize embedding models, combine vector and keyword retrieval with reciprocal rank fusion, rerank with cross-encoders, and assemble grounded, cited answers &mdash; then evaluate and operate the whole pipeline in production.</p>

Tools: python, chromadb, sentence-transformers, embeddings