What Comes After Reranking in a RAG Pipeline?
In a RAG pipeline, the results that survive reranking are passed to the language model as context for generating the answer. Strategically, teams that have already added reranking and want further gains look beyond query-time techniques to ingestion-time optimization, improving the index itself so retrieval returns better candidates before reranking runs. Reranking refines order; ingestion-time optimization improves what is available to refine.
The pipeline step after reranking
Mechanically, reranking is usually the last retrieval step. The top reranked documents become the context passed to the language model, which generates the final answer.
The strategic next lever, and why it can replace the step
Teams add reranking to improve relevance, then find its gains plateau because reranking can only reorder what retrieval already returned. The more effective move is earlier in the pipeline: clean the index at ingestion so retrieval returns the right candidates in the first place. When the index is clean from the start, the reranking step it was compensating for often becomes unnecessary. Green Vectors applies this by eliminating redundancy at ingestion, raising first-pass relevance so reranking is optional for most workloads.