What is Postprocessing?
Postprocessing refers to operations performed on retrieval results after the initial search but before those results are used for generation or decision-making. These operations refine, filter, reorder, or transform retrieved content to improve quality, relevance, or usability. Postprocessing can significantly impact the effectiveness of RAG systems by ensuring that the most relevant and useful information is provided to the language model.
Common postprocessing operations include re-ranking results using more sophisticated scoring models, deduplicating similar or redundant results, filtering based on metadata or content quality signals, truncating to fit context window constraints, extracting or highlighting the most relevant passages within retrieved documents, and reformatting content for optimal presentation to the LLM. Some systems also implement fact verification or source quality assessment during postprocessing.
The postprocessing stage provides an opportunity to apply computationally expensive operations to a small set of retrieval candidates rather than the entire corpus. For example, while initial retrieval might use fast but approximate methods to identify hundreds of candidates, postprocessing could apply a slower but more accurate neural re-ranking model to the top results. This multi-stage approach, common in information retrieval, balances efficiency and quality by using appropriate algorithms at each stage based on the size of the candidate set.