Re-ranking candidate documents for a query by feeding them all into an LLM's context and letting it pick the most relevant.
In-context ranking (ICR) is a way of re-ordering candidate documents for a query by placing the query, the document list and a task description directly in a language model's context window and letting the model identify the most relevant items. It leans on the LLM's contextual understanding instead of a separate scoring model.
The catch is cost. The attention mechanism scales quadratically with input length, so doubling the number of documents can quadruple the compute — impractical for large candidate lists. This is the bottleneck that BlockRank tackles by exploiting the block-sparse structure of attention.
ICR matters for AI SEO because it's increasingly how relevance gets decided inside generative systems: not classic keyword scoring, but a model reading candidates and choosing. It's a core part of modern relevance engineering.
Paper: Scalable In-context Ranking with Generative Models
