Query Complexity Routing for AI Resource and Latency Control
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Solution Overview
Problem
Existing generative AI models consume excessive resources due to varying query complexities, leading to inefficient resource utilization and potential overload of higher-complexity models.
Innovation Solution
A system that classifies input queries based on complexity and selects appropriate AI models (higher or lower complexity) to optimize resource consumption, incorporating dynamic load balancing and tool identification processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single high-complexity AI model is used for all queries, then response quality is maintained, but resource consumption increases and model overload occurs
Solution Approach 1:
The system segments queries into different complexity categories using a classifier model that assigns complexity scores. Based on these scores, queries are routed to different AI models (e.g., smaller models for simple queries, larger models for complex queries), thereby segmenting the workload and reducing overall resource consumption while maintaining response quality.
Solution Approach 2:
The system changes the parameter of model complexity selection based on query complexity scores. By dynamically adjusting which AI model is deployed for a given query based on its complexity classification, the system optimizes the balance between response quality and resource consumption.
2Use of energy by moving object
If a single low-complexity AI model is used for all queries, then resource consumption is reduced, but response quality deteriorates for complex queries
Solution Approach 1:
The system segments queries by complexity and assigns different models accordingly. Simple queries use low-complexity models to save resources, while complex queries are routed to high-complexity models to maintain quality, thus avoiding the pitfall of using a single low-complexity model for all queries.
Solution Approach 2:
The system applies partial action by using high-complexity models only when necessary (for complex queries) rather than applying them universally. This partial deployment of computational resources ensures quality is maintained where needed while minimizing overall consumption.
3Reliability
If higher-complexity AI models are used for all queries, then response quality is maintained, but computational loading and latency increase
Solution Approach 1:
The system segments queries by complexity and routes them to appropriately sized models. This segmentation prevents computational latency by avoiding the use of high-complexity models for simple queries, thereby optimizing both response quality and speed.
Solution Approach 2:
The system dynamically changes the model complexity parameter based on query characteristics. By selecting models with appropriate complexity levels rather than uniformly deploying high-complexity models, the system reduces computational loading and latency while maintaining quality.
4Measurement precision
If query complexity classification is performed at multiple stages, then model selection accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary classification of query complexity using a dedicated classifier model before routing to the appropriate AI model. This preliminary action improves model selection accuracy and can be optimized in terms of processing time through efficient classification algorithms and threshold-based decision making.
Data Source
AI summary
Systems and methods for resource conservation based on query complexity are disclosed. An input query is received (e.g., via a chat interface) and provided to a response classifier, which is a machine-learning classifier that is trained to classify input queries with complexity scores that indicate how difficult it is likely to be for an artificial intelligence (AI) model to generate a response to the input query. If the complexity score exceeds a threshold score, the input query is provided to a first AI model (e.g., a relatively high-complexity AI model having a large number of parameters, relatively long response latencies, and/or other performance characteristics). If the complexity score does not exceed the threshold score, the input query is provided to a second AI model (e.g., a lower-complexity AI model having fewer parameters, shorter response latencies, and/or other differences in performance characteristics relative to the first AI model).