bm25

The two knobs that make or break a RAG system, and the ones candidates hand-wave. The signal is tuning chunking against recall and knowing exactly what dense retrieval misses that BM25 catches. Here is the reasoning, not the rules of thumb.

Dense vs sparse is the core retrieval tradeoff, and 'just use embeddings' silently loses every exact-match query. The signal is knowing what each representation captures and why production blends them. Here is the answer.

Pure vector search misses exact terms; pure keyword search misses meaning. The signal is knowing why each fails alone and how to merge two rankings on incomparable scales without hand-tuning weights. Here is the answer.

A bridge between classic DSA and modern RAG. The signal is building a working inverted index and a correct BM25 score by hand, reasoning about its complexity, and then knowing exactly when you would reach for embeddings and an ANN index instead.

Hybrid retrieval only beats either method if the fusion is tuned. The signal is knowing why you cannot just add BM25 and cosine scores, how RRF's k constant behaves, and when a learned weight beats rank fusion.