Reranking

A second-stage model that reorders retrieved chunks before the LLM writes. You need to get into the first-stage set or rerank never sees you.

First-stage is BM25/embeddings. That’s why nouns and unique facts matter.

You cannot submit to the reranker.

Examples

You’re in the index but never in the top 50 candidates. Rerank cannot save you. Fix retrieval features on the page.

Combining keyword (BM25) and vector retrieval. Many RAG stacks do this. Your nouns and your meaning both count.

Scoring a section, not the whole URL. AI Overviews already behave this way. Your H2 might win while the page “ranks” page 2.

RAG means the model looks up documents at answer time instead of relying only on what it memorized in training.

SEO analog? +

Getting into the candidate set is like getting crawled. Rerank is like the last relevance pass.

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