recursive retrieval

Recursive retrieval iteratively fetches documents, drilling into them or following references for deeper exploration. **Pattern**: Initial retrieval → analyze results → identify citations/references → retrieve those → continue until sufficient depth. **Use cases**: Research with citations (follow references), hierarchical content (summary → details), multi-part questions, complex reasoning chains. **Implementation**: Retrieval loop with early stopping based on: information sufficiency, maximum iterations, relevance threshold. **Types**: **Drill-down**: Start with high-level, retrieve more specific chunks. **Citation following**: Extract references from retrieved docs, fetch those. **Entity expansion**: Identify entities, retrieve more about them. **Tree exploration**: Build knowledge tree through iterative retrieval. **Agentic approach**: LLM decides when more retrieval needed and what to retrieve. **Challenges**: May diverge from original topic, computational expense, determining stop criteria. **Integration with RAG**: Self-RAG pattern where model evaluates if more retrieval needed. **Best practices**: Set maximum depth, maintain relevance scoring, cache intermediate results.