Category: AI Search & Retrieval
Definition
Re-Ranking is the process of evaluating and rearranging retrieved information so that the most relevant results appear first.
An AI search system may initially retrieve many potentially relevant documents or content chunks. A re-ranking process then scores those results more carefully based on their relevance to the user’s query.
Why It Matters
Initial retrieval is often designed to be fast and broad. Re-ranking provides a second layer of evaluation that can improve relevance.
In AI search and RAG systems, this can help ensure that the information ultimately passed to an AI model is more closely aligned with what the user is asking.
Example
A user asks:
“How do I change the billing address on my account?”
A retrieval system might find 20 potentially related documents, including articles about:
- Changing account information
- Updating payment methods
- Billing addresses
- Invoices
- Account settings
A re-ranking system evaluates these results and places the document specifically explaining how to change a billing address at the top.
Retrieval vs. Re-Ranking
Retrieval finds a set of potentially relevant information.
Re-ranking evaluates that set and determines which results a
