Data indexing
Organizing data for efficient retrieval, enabling rapid access to information in databases, search engines, and retrieval-augmented-generation-rag systems. Critical for reducing latency and improving accuracy in information retrieval.
Key techniques
- LLM-enhanced indexing: Using large-language-models to create semantic indexes (beyond keyword matching), improving recall in RAG systems (e.g., boosting recall from 50-60% to 95% by generating context-aware index representations).
- Structured query generation: LLMs transforming natural language queries into optimized, structured formats for precise matching against indexed data.
- Hybrid indexing: Combining traditional vector indexes with metadata tagging for multi-dimensional retrieval (e.g., filtering by document type + semantic relevance).
Related concepts
- Vector Indexing
- Embedding
- Information Retrieval
- retrieval-augmented-generation-rag
2026 04 14 Improving RAG accuracy for retrieval