How do you solve semantic chunking fragmentation when building RAG over complex PDF tables and technical documentation?

### Problem Statement Standard sliding-window chunking breaks markdown tables, code blocks, and section headers across chunk boundaries, leading to broken context retrieved by vector search. ### Implementation Blueprint 1. **Structure-Aware Parsing**: Use layout parsers (Unstructured, LlamaParse) to extract tabular markdown intact. 2. **Parent-Child Chunking**: Store small chunks for vector retrieval linked to larger parent context blocks for generation. 3. **Hybrid Search Reranking**: Combine sparse keyword search (BM25) with dense vector embeddings (pgvector/Qdrant) and pass top results to a Cohere Rerank model. ### Takeaway Document preprocessing and hybrid retrieval contribute more to RAG accuracy than upgrading the underlying LLM model. *Note: This question represents expanded technical inquiry iteration #2 within the RAG & Vector Databases topic area.*

### Problem Statement Standard sliding-window chunking breaks markdown tables, code blocks, and section headers across chunk boundaries, leading to broken context retrieved by vector search. ### Implementation Blueprint 1. **Structure-Aware Parsing**: Use layout parsers (Unstructured, LlamaParse) to extract tabular markdown intact. 2. **Parent-Child Chunking**: Store small chunks for vector retrieval linked to larger parent context blocks for generation. 3. **Hybrid Search Reranking**: Combine sparse keyword search (BM25) with dense vector embeddings (pgvector/Qdrant) and pass top results to a Cohere Rerank model. ### Takeaway Document preprocessing and hybrid retrieval contribute more to RAG accuracy than upgrading the underlying LLM model. *Note: This question represents expanded technical inquiry iteration #2 within the RAG & Vector Databases topic area.*