RAG & Search
Cross-encoder
AI that examines two pieces of information together to judge how closely they are related.
Example
A reranker reads a query together with a candidate passage.
Why people use it
It can compare a question and a possible answer passage in greater detail.
What you'll hear
“Read the question and passage together before ranking them.”
What this means for you
Use the added detail where it justifies the computing work.
Can you control it?
Developer-only
The people building or running the AI choose this setup. An everyday user generally needs their help to change how this part works.
Common questions
- Can this rescue a useful document that was never found?
- Not by comparing a shortlist alone. A missing document must first reach the set of items being compared.
- Why can it cost more to search this way?
- Each pair needs to be processed together, which adds work when there are many candidate passages.
- Can it be used after a faster search?
- Yes. A fast first search can narrow the choices before a more detailed comparison.