LLM Reranking — Use an LLM as Your Final Relevance Judge

I will implement LLM-based reranking for your retrieval pipeline — using an LLM to score retrieved documents for relevance to the query through pointwise relevance scoring, pairwise comparison, or listwise ranking, producing the highest-quality reranking available at the cost of higher latency and token expense than cross-encoder alternatives. LLM reranking outperforms cross-encoder reranking on queries requiring deep understanding — technical questions where relevance depends on reasoning about the document's content, nuanced queries where relevance is contextual, and queries in highly specialised domains where general cross-encoder models have limited training signal all benefit from an LLM's comprehensive language understanding.

The implementation covers pointwise LLM scoring prompts, pairwise comparison implementation for smaller candidate sets, listwise ranking for comprehensive reordering, latency optimisation through parallelisation, cost tracking, and quality comparison against cross-encoder reranking.