Moving beyond naive vector lookup: building adaptive multi-agent retrieval routers that decide when to query vector databases vs SQL databases.
An Agentic RAG architecture places a router LLM upstream to evaluate query intent before dispatching execution to specialized tools.
References & Further Reading:
Unpacking DeepSeek-R1's Multi-head Latent Attention (MLA), DeepSeekMoE 210B sparse routing, and group relative policy optimization (GRPO) reinforcement learning.
Sarah ChenHow Grouped-Query Attention (GQA) and Multi-Query Attention (MQA) reduce KV-cache memory bandwidth consumption in LLM inference.
Sarah ChenAvoid repeating $0.03 LLM queries by matching semantically similar user prompts in vector cache before hitting API endpoints.
Principal AI Architect. Former DeepMind Researcher specializing in Large Language Models and Prompt Engineering.
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Sarah Chen