Building an AI Fallback System: Optimizing LLM Usage
This article discusses a three-tier fallback system for handling user queries, using a rules engine, a cheap model (Claude Haiku), and a frontier model (GPT-4o) to optimize cost and performance.
Why it matters
This article provides a practical example of how to optimize the use of large language models to balance cost, performance, and quality.
Key Points
- 1Avoid sending every query through expensive frontier models like GPT-4o
- 2Implement a rules engine for deterministic lookups and a cheaper model for simple generation
- 3Use a classifier to route queries to the appropriate tier based on complexity
- 4Significant cost savings by avoiding unnecessary LLM usage
Details
The article explains how the author's team built a three-tier fallback system to handle user queries more efficiently. The first tier is a rules engine that performs deterministic lookups for simple queries like FAQs and booking status checks, which can be handled without using a language model. The second tier is a cheaper model called Claude Haiku, which is used for simple generation tasks like summaries and formatting. The third tier is the frontier model, GPT-4o, which is reserved for complex reasoning and analysis. A classifier is used to route queries to the appropriate tier based on complexity, with the goal of minimizing unnecessary usage of the expensive GPT-4o model. This approach led to significant cost savings compared to the initial deployment that sent all queries through GPT-4o.
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