Reranking
Words made simple
A few useful meanings before you read the explanation.
- Reranking
- Checking an initial set of search results again and putting the most useful ones nearer the top.
- LLM
- A model trained to work with language. It can write or summarize text, but its answer still needs checking against reliable information.
- Embedding
- A list of numbers that represents the meaning of some content. Similar meanings can produce nearby lists of numbers.
More words from this lesson
- Retrieval
- Finding and fetching information that is useful for the current question.
- Tenant
- A customer or organization whose data must stay separate from other customers in a shared application.
- Evaluation
- Checking results against examples and clear criteria to find whether the system works well and where it fails.
- Latency
- How long a request takes to return a result. Lower latency usually means a shorter wait for the user.
- Chunking
- Dividing a long document into smaller pieces so relevant parts can be found and sent to the model.
Overview
- Reranking is a second ranking stage that examines a short candidate list more carefully.
- A reranker often reads the query and each candidate together, allowing richer comparison than separate embeddings.
- It changes candidate order; it cannot recover a relevant document absent from the candidate list.
Why this matters
Initial retrieval prioritizes speed and recall, while the answer needs a small set of highly relevant evidence.
An everyday example
A recruiter first finds 50 possible candidates, then carefully reviews their applications before selecting five.
A technical example
Retrieve 50 chunks, rerank them, and send the best five nonduplicate passages to the LLM.
How it works
- Retrieve broadly
- Score query/candidate pairs
- Sort
- Diversify/deduplicate
- Construct bounded context.
Architecture and flow
Initial retrieval → reranker → selected evidence → generation.
The insurance assistant example
Prefer the applicable claim reporting clause over a superficially similar marketing article.
When to use it
When candidate recall is good but the final context includes weak or distracting matches.
When to avoid it
It will not solve missing ingestion, wrong permissions, or an unrelated embedding model.
Interview answer
“Reranking improves precision among retrieved candidates. I fix retrieval recall first and justify its additional latency and cost through evaluation.”
Benefits, trade-offs, and production considerations
- Cap candidate count and batch requests.
- Rerank scores are model-specific.
- Treat all candidate text as untrusted input and keep tenant boundaries in place before sending it to an external reranker.
Can you explain it without the notes?
Reranking
Use your own words. Explain an example and one limitation before checking the explanation.
Check your explanation
Reranking is a second ranking stage that examines a short candidate list more carefully. A reranker often reads the query and each candidate together, allowing richer comparison than separate embeddings. It changes candidate order; it cannot recover a…
Turn this concept into a clear answer.
Practice this conceptAdapted from 13_AI.MD · “Reranking”. Examples are study fragments; framework behavior can depend on your version.