Optimizing Tool Selection for LLM Workflows with Local, Learnable Routers
By
viksit
Crackling crust, pillowy middle. The kind of bagel that earns a second cup of coffee.
Summary
The article discusses the challenges of using large language models (LLMs) in workflows and proposes the use of local, learnable routers to optimize tool selection, reduce token overhead, and improve efficiency.
Key quotes
· 3 pulledThis structure is easy to reason about, simple to prototype, and generalizes well.
But it scales poorly.
Each LLM call incurs latency, cost, and token overhead.
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