Building a Modern Search Ranking Stack: From Embeddings to LLM-Powered Relevance
Search is no longer a string-matching problem. A query for "wireless headphones" on a product search engine is not just about finding items containing those two words — it is about surfacing the best result based on semantic relevance, product quality, user preferences, and real-time availability. The gap between BM25 keyword matching and what users actually expect has forced a complete rethinking of search architecture.
This post walks through the anatomy of a modern search ranking stack: a multi-stage pipeline that combines sparse lexical retrieval, dense semantic embeddings, reciprocal rank fusion, cross-encoder reranking, and LLM-powered listwise ranking. I built a working demo that benchmarks each stage on the Amazon ESCI product search dataset — proving the value of every layer with real numbers.