architecture hierarchical context

**Hierarchical context** is the **context organization method that represents information at multiple levels such as token, chunk, section, and document to improve long-input reasoning** - it helps models navigate large evidence sets with structured focus. **What Is Hierarchical context?** - **Definition**: Multi-level context representation where fine-grained content is linked to higher-level summaries. - **Hierarchy Levels**: Often includes sentence or chunk nodes, section summaries, and global document abstractions. - **Retrieval Interaction**: Supports coarse-to-fine evidence selection in RAG pipelines. - **Reasoning Role**: Improves navigation of large contexts by preserving structural relationships. **Why Hierarchical context Matters** - **Scalable Comprehension**: Hierarchies reduce cognitive load in very long prompt scenarios. - **Token Efficiency**: High-level summaries guide attention before consuming fine-grained detail. - **Answer Quality**: Structured context lowers omission risk for multi-part questions. - **Debuggability**: Hierarchical traces make evidence flow easier to inspect. - **Latency Optimization**: Coarse filtering reduces expensive detailed processing. **How It Is Used in Practice** - **Layered Indexing**: Build indexes for both summaries and raw chunks with shared provenance. - **Progressive Retrieval**: Retrieve top sections first, then fetch specific supporting passages. - **Synthesis Protocols**: Combine high-level plans with low-level citations during answer generation. Hierarchical context is **an effective structure for long-context RAG reasoning** - hierarchical organization improves scalability, relevance, and traceable evidence use.