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Adaptive RAG: Optimizing for Speed and Cost

DKDhaval Kurkutiya
1 min read
Adaptive RAG: Optimizing for Speed and Cost

As AI usage scales, cost and latency have become the primary bottlenecks. Adaptive RAG is the industry's answer to this challenge.

Intelligent Routing

Not every question needs a deep search of 10 million documents. Adaptive RAG uses a "Router" (often a smaller, faster LLM) to decide the necessary depth of retrieval:

  • Direct Answer: For simple facts already in the model's weights.
  • Fast RAG: A quick vector search for standard queries.
  • Deep Agentic RAG: For complex, multi-layered questions that require tool use and iteration.

Success in Production

By implementing Adaptive RAG, companies are seeing 40-60% reduction in token costs and significantly faster response times for their users. It's the difference between a "slow and expensive" AI and one that feels like a native part of the user experience.