What is LLM-based reranking?

LLM-based reranking uses a large language model to reassess which retrieved document chunks best answer a user's question. It usually runs after a fast retriever (BM25, vector, or hybrid search) has produced a manageable candidate list.

How does it work?

1. Retrieve candidates: Search broadly, for example returning 20 passages from authorized sources.
2. Present the question and candidates: Give the reranking LLM clear instructions to judge *relevance to the original question*, not simply topical similarity.
3. Rank candidates: The model scores passages or returns an ordered list of candidate IDs.
4. Validate the ranking: Keep only valid IDs from the supplied candidate set, deduplicate them, and retain source metadata.
5. Send selected evidence: Pass the strongest relevant passages to the answer-generation model.

Question → first-stage retrieval → LLM reranking → best evidence → grounded answer

Practical example

A customer asks: “Can I return an opened device after 30 days if it is defective?”

The initial retriever finds a general 30-day return policy, a warranty page, and a defective-product exception.

An LLM reranker can inspect the full conditional question and prioritize the defective-product exception over a more general, superficially similar passage.

Common LLM ranking approaches

• Pointwise: Evaluate each query–passage pair separately and assign a relevance judgment or score.
• Pairwise: Compare two candidate passages and choose which better answers the question; multiple comparisons produce a ranking.
• Listwise: Consider several passages together and return their IDs in relevance order. Large sets may need windowing or multiple passes.

These techniques have different cost, stability, and ranking-quality trade-offs.

LLM reranker versus cross-encoder

• Cross-encoder reranker: A purpose-built relevance model that jointly processes each query–chunk pair, often with efficient numerical scoring.
• LLM reranker: Uses generative or instruction-following models to judge relevance, sometimes comparing several passages in one prompt. It can follow complex relevance instructions but may consume more tokens and be slower.

Neither approach is always superior; benchmark both on the actual query distribution.

Production considerations

• Cost and latency: Prefer reranking a shortlist rather than the full corpus.
• Position bias: List ordering can influence LLM judgments; test prompt and ordering stability.
• Output reliability: Validate returned passage IDs and handle malformed or incomplete rankings.
• Candidate recall: Reranking cannot recover evidence absent from initial retrieval.
• Security: Check access permissions before sharing passages with the reranking model or service.
• Evaluation: Measure nDCG@K, MRR, grounded answer quality, token cost, and p95 latency.

Interview takeaway

LLM-based reranking asks a language model to judge the relevance of retrieved passages—pointwise, pairwise, or listwise—before selecting evidence for RAG generation. It can better account for nuanced questions, but brings extra latency, cost, and output-validation requirements.