Hierarchical RAG Pattern
Parent-Child Indexing for Production Systems
Overview
Most RAG implementations use flat chunk-and-embed approaches that lose narrative context. A 2-hour webinar discussing multiple interconnected topics gets fragmented into isolated chunks that don't preserve relationships between ideas.
The hierarchical RAG pattern solves this by structuring content into two levels: child chunks (idea-level) for precise retrieval and parent segments (context-level) for generation. This preserves the relationship between ideas while enabling accurate search.
The Problem
Simple chunk-and-embed approaches lose narrative context when processing long-form content like webinars, presentations, or articles.
Child chunks get isolated from parent context, breaking relationships between ideas. Queries like 'how did the approach to X evolve' become impossible to answer accurately.
- •Lost narrative flow between chunks
- •No relationship tracking between ideas
- •Context fragmentation in multi-topic content
- •Temporal evolution queries fail
The Solution: Parent-Child Indexing
Content is structured into two hierarchical levels:
1. **Child chunks** (200-400 tokens): Specific concepts, methodologies, examples - these are embedded and searched
2. **Parent segments** (800-1200 tokens): Larger narrative sections containing multiple child chunks - these provide context for generation
Architecture Flow
- Content ingestion: Video/audio → Transcript → Hierarchical chunking → Embedding generation
- Retrieval: Semantic search (child chunks) → Metadata filtering → Reranking → Parent reconstruction
- Generation: Retrieved parent segments → LLM synthesis → Citation assembly → Confidence scoring
Results & Outcomes
- •60% reduction in consultant prep time ($180K annual savings)
- •Preserved narrative context in multi-topic content
- •Temporal queries enabled (track topic evolution over time)
- •Citation accuracy improved (timestamped source links)
- •3 new service offerings identified through content gap analysis
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