Advanced RAG Retrieval: Cross-Encoders & Reranking

Standard RAG pipelines often yield mediocre results because their bi-encoders process queries and documents independently, missing crucial contextual relationships. A more advanced approach involves a two-stage process where a fast bi-encoder first retrieves a broad set of candidates, and a precise cross-encoder then reranks this smaller set. The cross-encoder achieves superior accuracy by concatenating the query and a document into a single input, allowing its attention mechanism to directly compare tokens and understand nuanced contradictions. This joint processing produces a direct relevance score, but its computational intensity makes it ideal for reranking a limited number of documents rather than searching an entire collection.