Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
Data Science
Towards Data Science·Aug 13·5 min read
Cut an Enterprise RAG Pipeline’s Latency and Cost by Calling the LLM Less, Not by Buying a Faster Model
TL;DR
An enterprise document intelligence pipeline can reduce latency and costs by minimizing calls to a large language model (LLM) for straightforward queries. Implementing a keyword matching signal can save approximately two seconds per question.
✦ Why It Matters
Implement a keyword matching system to streamline your document processing pipeline today.
Key Takeaways
- Implement a router to direct easy questions away from model calls.
- Use keyword scores to evaluate question complexity before processing.
- Tune confidence thresholds based on your specific corpus.
- Measure latency and cost savings to justify the routing approach.
How It Works
The router evaluates questions using a pre-computed keyword score, determining if a question can be answered directly without model calls. If the top score significantly exceeds the second-best score, the question is routed to a deterministic extractor, bypassing the model entirely.
This decision-making process is quick, running in about 0.1 milliseconds, compared to the multiple seconds required for full model processing.