Definition
Reranking
Reranking is a second-stage refinement in retrieval that reorders an initial set of candidate results by relevance to the query, using a more precise but costlier model. A fast first pass casts a wide net; the reranker then scores each candidate against the query directly, promoting the truly best matches to the top — sharply improving the quality of retrieval.
Key takeaways
- Reranking is a second-stage retrieval step that reorders an initial candidate set by relevance using a more precise but costlier model.
- A fast vector first pass casts a wide net; the reranker scores the query and each candidate jointly to surface the true best matches.
- It runs only on the shortlist because it is too expensive over a whole corpus — buying accuracy where it counts.
- It is high-leverage for retrieval-augmented generation: better top results mean better grounded context and better answers.
Retrieval is usually two stages because of a speed-versus-accuracy trade-off. The first stage uses fast vector similarity to pull a broad candidate set — say the top fifty embeddings nearest the query — which is quick but approximate, since it compares pre-computed vectors rather than examining the query and document together. Good recall, rough ordering.
Reranking is the precision second stage. A reranker model takes the query and each candidate as a pair and scores how well they actually match, considering them jointly rather than as independent vectors. This is far more accurate per comparison but too expensive to run over an entire corpus — which is exactly why it is applied only to the shortlist the first stage produced.
The payoff is the quality of the final top results, which matters enormously for retrieval-augmented generation: an AI grounds its answer on the few passages handed to it, so promoting the genuinely most relevant ones into that small window directly improves the answer. Reranking is one of the highest-leverage, lowest-effort upgrades to a retrieval pipeline.
Planoda reranks the top candidates from its vector retrieval before grounding an AI response, so answers draw on the most relevant tenant-scoped context rather than merely the nearest vectors.
Related terms
- EmbeddingAn embedding is a numeric vector that represents a piece of content — text, image, or other data — as a point in high-dimensional space, positioned so that similar items land near each other. Produced by a model trained to capture meaning, embeddings let software compare content by semantic similarity rather than by matching exact words.
- Vector DatabaseA vector database stores data as high-dimensional embeddings and retrieves items by similarity rather than exact match. Given a query vector, it returns the nearest stored vectors — the records most similar in meaning. Specialized indexes make this fast across millions of items, making vector databases the retrieval backbone of semantic search and AI applications.
- Semantic SearchSemantic search finds results by meaning rather than exact keywords, using vector embeddings that place similar concepts near each other in mathematical space. A query for 'login broken' can surface an issue titled 'users can't authenticate' even with no shared words. It powers more relevant search and is the retrieval layer behind many AI features.
- Retrieval-Augmented Generation (RAG)Retrieval-augmented generation (RAG) grounds a language model's answers in your own data by retrieving the most relevant documents at query time and feeding them into the model's context before it responds. Instead of relying solely on what the model memorized in training, RAG lets it answer from current, authoritative, private sources — sharply reducing hallucination.
- GroundingGrounding is the practice of tying an AI model's responses to verifiable, external source data rather than relying solely on what it absorbed during training. By supplying relevant, current, authoritative context at query time — and ideally citing it — grounding reduces hallucination and keeps answers accurate, traceable, and specific to the user's actual data.