Reranking
A post-processing step that re-evaluates initial search results to reorder them by higher relevance
#Reranking#Re-ranking#Search post-processing#Reordering#RAG
What is Reranking?
Reranking is the stage where document candidates retrieved by a first-pass search are re-scored and reordered.
Typically, vector search or hybrid search pulls the Top-N candidates quickly, and then a more sophisticated model or rule set reshuffles them into a final ranked list.
Why Is It Needed?
First-pass retrieval is optimized for speed, so irrelevant documents may sneak into the top results. Applying a reranker improves the quality of the context fed into answer generation, which in turn lifts RAG answer accuracy.
Practical Application Points
- First-pass retrieval: fast candidate recall (e.g., Top-20)
- Second-pass reranking: accuracy-first reordering (e.g., pick Top-5)
- Monitor metrics together: Precision@K, ground-truth inclusion rate, and response latency
Related terms
Natural Language Processing
Chunk
A text segment created by splitting long documents into meaningful units for retrieval and generation
Natural Language Processing
Hybrid Search
A search strategy that combines vector and keyword retrieval to raise both precision and recall
Natural Language Processing
RAG (Retrieval-Augmented Generation)
A technique that enhances LLM responses by retrieving relevant external information before generating an answer
Natural Language Processing
AGI (Artificial General Intelligence)
A hypothetical AI system capable of performing any intellectual task a human can
Natural Language Processing
AI Agent
An autonomous AI system that can plan, use tools, and take actions to achieve goals
Natural Language Processing
Attention
A mechanism that allows AI models to focus on the most relevant parts of the input when producing output