Reranking
Reranking takes retrieved candidates and scores them again with a stronger relevance signal.
Reranking takes retrieved candidates and scores them again with a stronger relevance signal.
Get a broad candidate set.
Score query–chunk relevance more carefully.
Keep only the best evidence.
Understand the working model
Vector similarity is fast and useful, but the nearest chunk is not always the most answerable chunk. A reranker can compare the query and each candidate more directly before context is assembled.
Reranking is especially useful when documents contain overlapping language, many similar sections or short chunks that share vocabulary but differ in meaning.
Practice
Apply the pattern above to one real task you already do. Keep the first version small enough that you can inspect every input and output.
Do not add reranking before you can measure whether basic retrieval is actually failing.
Use reranking when you need better ordering of a broad candidate set, then keep the final context small.