Menu
Use a two-stage retrieval pipeline with reranking to balance recall, precision, and latency.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
Use this skill when building, debugging, or improving Retrieval-Augmented Generation systems, including chunking, vector database selection, hybrid search, reranking, multimodal RAG, code documentation RAG, retrieval latency, and production RAG architecture.
Chunk nested documents into parent-child levels so retrieval can move from broad sections to fine-grained passages.
Use semantic boundaries and embedding similarity to chunk text for higher-relevance retrieval.
Route RAG chunking decisions across semantic, hierarchical, sliding-window, contextual-header, and framework-selection strategies.
Use overlapping windows to preserve context across chunk boundaries while controlling retrieval size.
Reduce retrieval latency with caching, batching, and index-level optimization.
| name | multi-pass-retrieval-with-reranking |
| title | Multi-Pass Retrieval with Reranking |
| description | Use a two-stage retrieval pipeline with reranking to balance recall, precision, and latency. |
| allowed-tools | ["Read","Grep","Glob","Bash"] |
| category | retrieval-strategies |
| tags | ["reranking","cross-encoder","two-stage","relevance"] |
Multi-pass retrieval with reranking is a two-stage approach that first retrieves a broad set of candidates using fast bi-encoder search, then refines them using a more accurate but slower cross-encoder reranker. This balances recall and relevance while maintaining acceptable latency.
Single-stage retrieval has inherent trade-offs:
Implement the first-stage retrieval using your vector database.
Why: Bi-encoder retrieval is fast and scalable but produces approximate scores - suitable for the first pass to narrow candidates.
Implement the second-stage reranking for accurate scoring.
Why: Cross-encoders produce more accurate relevance scores by jointly encoding query-document pairs, but are too slow for full-scale retrieval.
Dynamically adjust retrieval parameters based on query characteristics.
Why: Adaptive retrieval ensures complex queries get more candidates for reranking while simple queries remain fast.
Combine all components into a complete retrieval pipeline.
Why: A unified pipeline with timing and explanation makes it easy to optimize and debug the retrieval system.
Add caching and optimizations for production use.
Why: Caching reduces computation for repeated queries, which is common in production systems.
For working examples, see Sentence Transformers CrossEncoder docs, Sentence Transformers rerankers, Sentence Transformers retrieve & re-rank, and the MonoT5 paper.
