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Hierarchical Retrieval and Chunking: Scaling Knowledge Without Noise
A technical guide to hierarchical retrieval, chunking strategies, and multi-stage evidence selection.
Why Flat Chunking Breaks at Scale
Flat chunking ignores document structure and context hierarchy.
As corpora grow, noise increases and relevant evidence is buried.
Hierarchical Retrieval Basics
Use a two-stage approach: coarse retrieval, then fine-grained selection.
Hierarchical indexes preserve context while reducing search overhead.
Chunk Size and Overlap
Large chunks preserve context but reduce precision.
Small chunks improve precision but risk losing key dependencies.
Multi-Stage Evidence Selection
Rerank at each stage to keep the best evidence.
Combine semantic and lexical signals for stability.
Evaluation and Tuning
Measure evidence coverage and answer correctness.
Tune chunking based on user queries and domain structure.
Hierarchy Design
Define document, section, and paragraph levels with explicit IDs.
Use parent-child links so evidence can be traced back to sources.
Keep top-level summaries for fast coarse retrieval.
Tune hierarchy depth based on document complexity.
Store section metadata to improve relevance filtering.
Use consistent chunk boundaries to stabilize caching.
Measure retrieval time per layer to keep latency predictable.
Keep a fallback flat index for edge cases and debugging.
Chunk Evaluation
Score chunk relevance against ground-truth citations when available.
Track overlap rates to avoid redundant evidence.
Measure recall at each layer to identify pruning issues.
Test chunk boundaries on long documents with repeated topics.
Review failure cases where key evidence is split across chunks.
Use adaptive chunking for tables, lists, and structured content.
Log chunk selection decisions for auditability.
Monitor chunk size drift to prevent silent quality changes.
Compare chunking strategies on latency as well as accuracy.
Use reviewer feedback to refine chunk boundaries over time.
Audit chunk provenance so sources stay traceable.
Capture chunk heatmaps to see which sections drive answers.
Keep a regression suite for chunking changes to avoid surprises.
Tune overlap windows to balance recall and context size.
FAQ: Hierarchical Retrieval
Is hierarchical retrieval always better? For large corpora, usually yes.
What is the biggest risk? Over-pruning relevant context too early.
What is the fastest win? Add section-level indexing before paragraph-level retrieval.
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