A concrete example
Vector search hands me 50 chunks. A reranker sorts them by real relevance, and I keep only the top 5.
Why it matters
Without reranking, your RAG answers with noise. With it, it answers with signal. That's often the difference between working and breaking.
You see it in semantic search engines and RAG systems that need to cite precisely.
Don't mix it up with
RAG: RAG lets an AI answer using documents pulled up at the moment you ask the question.
Embedding: An embedding turns a piece of text into a list of numbers so you can compare its meaning with other texts.
Common mistakes
- Sending 50 chunks to the model without reranking them (terrible quality).
- Confusing reranker and embedding: they're not the same models.
- Forgetting that reranking costs you latency.
Quick checklist
- First I check whether the word names a concept, a tool, a risk, or a metric.
- I tie it to a concrete case: Vector search hands me 50 chunks. A reranker sorts them by real relevance, and I keep only the top 5.
- I keep the main trap in mind: Sending 50 chunks to the model without reranking them (terrible quality).
Quick questions
What is Reranking in AI?
Reranking re-sorts the results you found so the model gets the most relevant passages.
Where will I run into Reranking?
You see it in semantic search engines and RAG systems that need to cite precisely.
Which word should I read next?
Start with RAG, Embedding, Vector database.