AI Architecture
Parent Document Retriever: Small Chunks for Retrieval, Large Chunks for Context
Improve RAG recall and coherence: index 200-character granular chunks for vector matching, but return the full parent section to the LLM.
The Small-to-Big Retrieval Principle
Small snippets yield higher cosine similarity against specific queries, while surrounding parent paragraphs give the LLM full context to construct accurate answers.
Related Technical Guides
Deepen your understanding with these closely related production architectures and tutorials:
2026 Frontier Stack: LangGraph Checkpointing, Mem0 Scoped Memory & Prefix Caching FinOps
Architect enterprise AI agents with decoupled lifecycles: prompt KV-cache optimization (90% savings), LangGraph state persistence, and Mem0 long-term memory.
Model Context Protocol (MCP) Guide: Connecting LLMs to Local Databases and Tools
Learn the open-source Model Context Protocol (MCP) standard created by Anthropic and how it turns LLMs into extensible agents connected to your infrastructure.
Prompt Caching Architecture: Slashing LLM API Costs and Latency by 90% via KV-Cache Reuse
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