Cut an Enterprise RAG Pipeline’s Latency and Cost by Calling the LLM Less, Not by Buying a Faster Model

A recent optimization in an Enterprise RAG pipeline has reduced latency and cost by minimizing the number of times a Large Language Model (LLM) is called, rather than relying on a faster model. By implementing a per-question signal that routes easy questions past the model, the pipeline can save around two seconds per query. This approach allows for more efficient use of resources, reducing unnecessary latency. The practical implication for engineers building AI systems is that optimizing the pipeline and reducing unnecessary model calls can have a significant impact on performance and cost.

⚡ Key Takeaways

• The optimization saves around two seconds per query for easy questions that can be routed past the model using a keyword match.
• The pipeline uses a per-question signal to determine whether to call the LLM or not.
• Reducing the number of LLM calls can decrease latency and cost, rather than relying on a faster model.
• The approach can be applied to Enterprise RAG pipelines to improve efficiency.
• The pipeline from Article 9 is an example of a system that can benefit from this optimization.

This optimization can have a significant impact on the performance and cost of Enterprise RAG pipelines, allowing engineers to build more efficient and scalable AI systems. By reducing unnecessary model calls, engineers can improve the overall latency and cost of their pipelines.

✅ Practical Steps

1. Implement a per-question signal to route easy questions past the LLM in your Enterprise RAG pipeline.
2. Analyze your pipeline to identify opportunities to reduce unnecessary model calls and optimize latency and cost.
3. Apply the concepts from this article to your own system design to improve efficiency and scalability.

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