Prompt Processing Units for Cost- and Delay-Aware LLM Routing
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Solution Overview
Problem
Existing mechanisms lack a data-driven approach for selecting the right large language model (LLM) and its parameters, leading to suboptimal model utilization that impacts system performance, operational costs, and delays.
Innovation Solution
Implementing prompt processing units (PPUs) to analyze incoming prompts, extract metadata, and dynamically select the most suitable LLM and parameters based on performance, cost, and delay metrics, leveraging normalized metrics and external information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a default model is applied across all prompts, then system simplicity is maintained, but model utilization becomes suboptimal impacting performance and costs
Solution Approach 1:
The patent implements dynamic model selection where the system automatically chooses different LLMs based on real-time analysis of prompt characteristics, task requirements, and current system state. This transforms the static default model approach into a dynamic routing system that adapts to each incoming prompt, resolving the contradiction between system simplicity and model utilization efficiency.
Solution Approach 2:
The system changes the parameter of model selection from a fixed default to a variable decision based on multiple factors including prompt metadata, task type, model performance metrics, and cost considerations. This parameter change enables optimal model matching while maintaining automated decision-making that doesn't significantly increase operational complexity.
2Device complexity
If model selection is based on user guesswork, then system complexity is reduced, but system performance deteriorates due to suboptimal model choices
Solution Approach 1:
The patent incorporates feedback mechanisms where the system continuously monitors model performance metrics, prompt characteristics, and outcome quality. This feedback loop enables the system to learn from past selections and improve future model choices, ensuring reliable performance without requiring complex manual configuration or user expertise.
Solution Approach 2:
The system performs self-service model selection by automatically analyzing prompt requirements and selecting appropriate models without user intervention. This eliminates reliance on user guesswork while keeping the selection mechanism relatively simple through automated rule-based and data-driven decision-making.
3Productivity
If diverse LLMs are used for different prompts, then model utilization optimization is achieved, but system complexity increases
Solution Approach 1:
The patent segments the model selection process into distinct analytical components: prompt metadata extraction, task classification, model performance evaluation, and routing decision-making. This segmentation allows the system to handle diverse LLM selection through modular, manageable steps rather than a monolithic complex system.
Solution Approach 2:
The system introduces an intermediary routing layer between prompt intake and model execution. This intermediary component analyzes prompts and mediates the selection process, managing the complexity of diverse LLM orchestration while maintaining clear separation of concerns and simplified interfaces for users.
4Productivity
If automated model selection is implemented, then model utilization is optimized, but computational overhead and delays increase
Solution Approach 1:
The patent implements preliminary action by pre-analyzing prompt characteristics and pre-evaluating model suitability before actual model execution. The system extracts metadata and classifies tasks in advance, so that when a prompt arrives, the model selection can be made quickly based on pre-computed information rather than analyzing everything from scratch.
Solution Approach 2:
The system applies local quality by focusing computational effort on the specific characteristics of each incoming prompt rather than performing comprehensive analysis for all prompts uniformly. The model selection process adapts its analysis depth based on prompt complexity and urgency, reducing unnecessary computational overhead for straightforward cases.
Data Source
AI summary
In one implementation, a device may identify a task requested by a prompt for input to a language model. The device may compute, based on the task, two or more estimated performance metrics for each of a plurality of candidate language models associated with that model performing the task. The device may select a particular language model from among the plurality of candidate language models to optimize the two or more estimated performance metrics. The device may cause the prompt to be sent to the particular language model for performance of the task.