reranking

The modal Applied AI design round. Anyone can draw embed-retrieve-generate. The signal is in chunking, hybrid retrieval, the rerank/latency tradeoff, and how you prove it works. Here is the structure that scores.

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.

Reranking is the cheapest large win in RAG quality: retrieve broadly, then reorder precisely. The signal is the bi-encoder-versus-cross-encoder distinction and the retrieve-then-rerank two-stage design. Here is the answer.

Cranking up top-k raises recall but drowns the answer in noise, and a tight reranker raises precision but can drop the one chunk you needed. The signal is knowing which metric caps the system and how to move both with a retrieve-wide-then-rerank shape.

A 0.87 cosine score on a chunk that answers nothing is not a bug in your vector database. It is the embedding model telling you exactly what it was trained to tell you. Here is how to read that signal instead of thresholding it away.

Enterprise search lives or dies on permissions, freshness, and connecting to thirty messy SaaS sources; retrieval quality is table stakes. Learn how to fan out across connectors, enforce per-user access at query time, and blend lexical with vector search for results people trust.