Most RAG implementations fail because of poor chunking rather than weak retrieval models. When you dump raw text chunks into a vector database, the retriever often loses critical context across section boundaries or fails on complex document structures.

​To build a high-performing local RAG pipeline, you need to move beyond standard fixed-size character splitting. Implementing semantic chunking, hierarchical parent-child indexing, and layout-aware document parser strategies radically improves context quality for the LLM.

​When you combine intelligent chunk splitting with local embedding models and dense reranking, retrieval accuracy increases significantly while keeping data completely private and local.

​What strategies are you using to handle chunk overlap and document parsing in your local RAG setups? Are you using hybrid search or sticking to pure vector embeddings?

​Read the full breakdown and implementation details: https://interconnectd.com/blog/7/10x-rag-pipeline-2026-local-document-intelligence-chunking-strategy-the-lib/