What is a reranker and do I need one?
If you’re building AI systems like RAG or AI Agents, you’re probably familiar with semantic search and keyword search concepts.
Keyword Search (BM25): lightning-fast inverted-index lookups, perfect for exact matches (“error handling in Python”), but recall drops when phrasing shifts (“how to catch exceptions”).
Semantic Similarity: nearest-neighbor on precomputed vector embeddings (or bi-encoders), much better at conceptual queries.
At ZeroEntropy, we combine those two methods in a hybrid setup to maximize recall. But recall alone isn’t enough. You might retrieve the needle-in-the-haystack, but if it sits at position 237, your LLM, or your users, will never see it.
What is a reranker?
A reranker is a cross-encoder neural network that refines search results by re-scoring and reordering them based on query–document relevance.
Because the model sees the full text of both query and document at the same time, it is much more context-aware than first-stage methods where the document might have been encoded without knowledge of the specific query.
It can weigh the exact phrasing and context of query terms in the document and pick up subtle semantic relationships that the first stage search might miss.
Rerankers are modular in the sense that they can be used after any initial search pipeline, whether semantic, keyword-based, or hybrid.
Why add a reranker?
Using a first stage retrieval method based on keywords or vector similarity, you can fetch N candidate documents in milliseconds. As N grows, recall improves, pulling in the entire corpus would give you a recall of 100 percent. But recall alone does not guarantee a useful result set. Those N candidates still need to be processed by a human or by an LLM or agent, and precision, the share of truly relevant documents among the ones you return, matters just as much. Retrieving everything maximizes recall but collapses precision to zero.
Low precision means feeding your downstream system a flood of noise, which makes LLMs more prone to hallucination and buries the “needle in the haystack.” Users typically only inspect the top 10 search results; anything beyond that feels like manual scanning and wastes time. Similarly, the “lost-in-the-middle” problem makes LLMs forget important pieces of information buried in a context that’s too large.
The solution is to introduce a reranker: start with a fast first stage to collect a broad set of hits, then apply a scoring model that promotes the most relevant candidates so that your top K results are the ones you actually need.
Performance & Scaling Trade-Offs
Vector Search (Logarithmic Scaling)
Embeddings precomputed offline.
ANN algorithms (HNSW, IVF) search millions of vectors in ≈O(log N) time.
Great for broad recall at low latency.
Cross-Encoder Reranking (Linear in Candidates)
Each query–doc pair is fully processed, O(M) cost for M candidates.
Impractical over the entire corpus, but efficient on a narrowed set (e.g., M≤100).
Balances quality vs. compute: fast first stage + targeted rerank.
When are rerankers most useful?
In most serious RAG or agent applications, especially those dealing with long, complex queries or high-stakes decisions, precision is paramount. A few hundred extra milliseconds to rerank can save you from LLM hallucinations and angry users.
There are situations where rerankers are even more useful:
Long, complex queries.
“Extract all clauses from vendor agreements signed in Q4 2024 that impose liquidated damages for late delivery and override any confidentiality provisions.”
A keyword pass will return every Q4 2024 agreement and any mention of “liquidated” or “confidentiality,” but it can’t prioritize the handful of clauses that satisfy both conditions. A reranker can learn to spot that rare intersection and boost those exact snippets to the top.
Adjacent-concept queries
“Show me support tickets about authentication failures”
If most tickets mention “denied access” or “invalid tokens” then a semantic search might not rank those very high. A reranker would be able to reason across all tickets retrieved and catch those adjacent terms and surface truly relevant cases.
Fuzzy-filtering queries
“Find all patient records where age > 65.”
Ages might be written as “born before 1959,” “sixty-eight,” or even stored under a “DOB” field. Also, embeddings and keyword matches might match any “65” number unrelated to an age. A reranker might recognize those fuzzy expressions and false positives and only return truly “over-65” records.py, no changes are needed, zerank-1 will be automatically running for every query sent.
If you’re building anything retrieval-heavy, this will make your search results significantly more accurate.
