Sentence Window Retrieval

A RAG enhancement technique from Anthropic that adds chunk-specific explanatory context to each document chunk before embedding. Contextual Retrieval reduces retrieval failure rates by 49% and improves accuracy by 67% compared to traditional RAG methods.

A RAG technique that indexes small chunks for precise matching but retrieves larger parent documents for LLM context. Balances retrieval precision with comprehensive context by separating indexing granularity from context size.

Advanced retrieval approach combining dense vectors, sparse vectors, and reranking in a multi-stage pipeline, achieving up to 48% better performance than single-method retrieval.

LangChain's hierarchical text chunking strategy achieving 85-90% accuracy by recursively splitting using progressively finer separators to preserve semantic boundaries.

The process of determining optimal text segment sizes for embedding and retrieval in vector databases. Chunk size significantly impacts RAG quality, balancing between capturing complete context (larger chunks) and retrieval precision (smaller chunks), typically ranging from 256 to 1024 tokens.

A two-stage retrieval process where initial candidates from vector search are reordered using more sophisticated models like cross-encoders. Reranking significantly improves result quality by applying computationally expensive models to a small set of candidates, commonly used in RAG systems and search applications.