AI search explained: indexing, retrieval and reranking

This post walks through three retrieval approaches on a synthetic ten-note dataset from eight tools: text search using an analyzer and inverted index, embedding search using an embedding model and vector database, and embedding search with a reranker that reorders a shortlist by relevance. It shows text search missing paraphrases, embedding search surfacing semantically similar notes but ranking a refund how-to above the actual reasons, and reranking putting the relevant notes on top. It concludes with guidance on when to use each approach and notes that production systems often combine text and embedding search via hybrid search with reciprocal rank fusion before reranking.