LLM Routing System for Query Capability Matching
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Solution Overview
Problem
Users face challenges in selecting the best large language model provider among many options, as each provider has unique APIs, user interfaces, functionalities, fee models, and requirements, leading to inefficient query customization and potential suboptimal cost, efficiency, or accuracy.
Innovation Solution
A method that involves receiving a query, determining its associated capability using a machine learning model or segmentation algorithm, and then using a routing system to identify the most suitable large language model provider based on capabilities, cost, quality, or accuracy, before providing the query and receiving a response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users customize queries for each large language model provider, then query accuracy may improve, but device complexity and time consumption increase
Solution Approach 1:
The patent introduces a routing system as an intermediary between the user and multiple LLM providers. This routing system receives user queries, determines capabilities using machine learning models or segmentation algorithms, and routes queries to the most appropriate provider automatically, eliminating the need for users to manually customize queries for each provider while maintaining high accuracy
2Productivity
If users manually select and customize queries for each provider, then query optimization may improve, but loss of time increases
Solution Approach 1:
The routing system performs preliminary actions by pre-determining provider capabilities and pre-establishing routing rules before actual query processing. When a query arrives, the system quickly matches it against pre-computed capability profiles and routing criteria, enabling fast automatic routing without requiring real-time manual analysis or customization time
3Ease of operation
If a routing system automatically selects providers, then ease of operation improves, but measurement precision of query matching may worsen
Solution Approach 1:
The routing system incorporates feedback mechanisms where it receives responses from LLM providers and uses this information to refine future routing decisions. The system tracks performance metrics from provider responses and adjusts capability determinations accordingly, ensuring that automatic routing becomes increasingly accurate over time while maintaining ease of use
Data Source
AI summary
A method includes: receiving a query; determining a capability associated with the query using at least one of a capability machine learning model or a segmentation algorithm; determining, using a routing system, a large language model provider, among a plurality of large language model providers, that best matches the capability associated with the query; providing the query to the large language model provider; and receiving a response from the large language model provider.