Split-and-Merge Retrieval

Split-and-Merge Retrieval is a strategy for handling oversized documents in Retrieval-Augmented Generation and search pipelines. The method splits a long text into overlap-aware chunks that fit the retriever’s embedding window, performs independent semantic searches on each chunk, and then merges the top-ranked hits into a unified result list before passing context to the language model. This preserves recall on fine-grained facts buried deep in a PDF while avoiding token-waste on irrelevant sections. Key parameters include chunk length, overlap size, max-marginal-relevance (MMR) deduping, and a merge heuristic—weighted score, reciprocal rank fusion, or learned re-ranker. Metrics such as recall@k and token-cost ratio gauge benefit; gains depend on the corpus and the chunking strategy and have to be measured per deployment. Split-and-Merge Retrieval pairs well with hierarchical chunking and sliding-window attention; latency depends on index size, the re-ranker, and the hardware.

Related terms

• Agentic Retrieval-Augmented Generation
• C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models
• Cache Retrieval
• Hire Retrieval-Augmented Generation (RAG) developers
• IM-RAG (Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues)

Related services: RAG development service, Agentic AI consulting.

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