The Complete Guide to Hybrid Search in RAG (BM25 + Embeddings + Reranker)

Why watch

A comprehensive tutorial building a production hybrid retrieval system from scratch, combining BM25 sparse search, dense embeddings, reciprocal rank fusion, and Cohere reranking. Covers the FinanceQA dataset from BEIR benchmark, implements each component in Python, and evaluates all approaches using NDCG@10. Ideal for AI engineers building RAG systems who want to understand how to combine multiple retrieval methods.