Prompt Routing With Behavioral Model Characterization
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Solution Overview
Problem
Selecting the best foundation model for a given prompt is challenging due to the black-box nature of these models, which makes it difficult to determine the optimal model for a specific prompt using conventional programmatic characterization techniques.
Innovation Solution
A prompt routing system and method that determines training data, selects a router, and uses the router to receive a runtime prompt, predict performance scores for candidate models, and optionally predict operational metrics to select the best model for the prompt, without running the prompt through each candidate model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional programmatic characterization techniques are used to select foundation models, then the selection process becomes systematic and repeatable, but the black-box nature of foundation models prevents accurate characterization and selection
Solution Approach 1:
The patent introduces an intermediary characterization system that acts as a mediator between the black-box foundation models and the selection process. This intermediary layer captures model behaviors through standardized interactions and representations, enabling systematic comparison without requiring internal model transparency. The intermediary translates opaque model outputs into comparable metrics that facilitate deterministic selection.
Solution Approach 2:
The patent transforms the selection problem by changing the parameters used to characterize models. Instead of attempting to measure internal model properties that are inaccessible due to the black-box nature, the system changes to measuring external behavioral parameters through standardized prompts and responses. This parameter transformation enables systematic characterization while respecting the black-box constraint.
2Reliability
If multiple foundation models are evaluated for each prompt to ensure optimal selection, then response quality improves, but computational time and resources increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-characterizing foundation models using representative prompts before actual deployment. The characterization results are stored and reused for subsequent prompt routing decisions. This preliminary characterization eliminates the need to re-evaluate models for every new prompt, significantly reducing computational time while maintaining reliable model selection based on pre-computed performance data.
Solution Approach 2:
The patent creates copies of model performance characteristics through standardized representations and metrics. Instead of running actual models repeatedly to evaluate them, the system uses copied performance data from preliminary characterizations. These copies enable rapid comparison and selection without the computational cost of actual model execution for each evaluation.
3Reliability
If foundation models are treated as black-box systems, then model security and intellectual property are protected, but programmatic selection and optimization become impossible
Solution Approach 1:
The patent segments the model selection process into distinct components: (1) a black-box model interface that preserves security and intellectual property, and (2) an external characterization system that captures behavioral metrics. This segmentation allows the system to maintain model confidentiality while enabling programmatic selection through the separate characterization layer that operates on observable model behaviors rather than internal structures.
Data Source
AI summary
In variants, the method can include determining training data, determining a router, and using the router. In variants, using the router can include receiving a runtime prompt, predicting performance scores for the runtime prompt for each of a set of candidate models, optionally predicting operational metrics for responding to the runtime prompt for each of the set of candidate models, selecting a candidate model based on the predicted performance scores and optionally the predicted operational metrics, and optionally determining a response based on the runtime prompt.