Adaptive Prompt Virtualization for Cross-LLM Portability
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Solution Overview
Problem
Existing solutions for interacting with Large Language Models (LLMs) lack prompt portability across different models, fail to address model-specific behavior nuances, and lack a feedback mechanism for continuous adaptation based on real-world usage and user feedback, leading to inefficiencies and potential errors when switching between LLMs.
Innovation Solution
A virtualization layer that automates prompt tailoring and selection for optimal responses across multiple LLMs, utilizing natural language understanding, contextual data from user interaction history, and a feedback loop to refine model selection criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prompts are customized for each specific LLM model, then response quality is improved, but prompt portability across different models deteriorates
Solution Approach 1:
The patent creates a universal prompt framework that can be adapted across multiple LLM models. Instead of creating entirely separate prompts for each model, the system designs a core prompt structure that can be customized with model-specific parameters and adjustments, allowing the same basic prompt to work across different models while maintaining optimized performance for each.
Solution Approach 2:
The patent applies local quality by making prompts partially standardized and partially customized. The core structure and logic remain consistent across models (local quality), while specific parameters, formatting, and certain elements are adjusted for each model's characteristics (local customization), achieving both portability and model-optimized performance.
2Measurement precision
If a virtualization layer with feedback loop is implemented, then continuous adaptation and response quality are improved, but system complexity increases
Solution Approach 1:
The patent implements a feedback loop where the system monitors the performance and responses of LLM models, uses this information to refine and adjust prompts, and continuously improves the virtualization layer's ability to select and customize prompts. This feedback mechanism enables automatic adaptation without requiring manual intervention for each new model or use case.
Solution Approach 2:
The virtualization layer is designed to automatically manage prompt customization and model selection without requiring extensive manual configuration. The system self-adjusts based on performance data, automatically adapting to new models and optimizing prompts through the feedback loop, reducing the need for complex manual management.
3Ease of operation
If contextual data from user interaction history is integrated, then prompt relevance and user experience are improved, but data processing requirements and system resources increase
Solution Approach 1:
The patent extracts only the most relevant contextual information from user interaction history rather than processing all available data. The system identifies and extracts key patterns, user preferences, and relevant context that directly impact prompt effectiveness, discarding redundant information to reduce processing requirements while maintaining user experience quality.
Data Source
AI summary
Embodiments of the present invention provide computer-implemented methods, computer program product, and computer systems. One or more processors analyze user prompts using one or more natural language understanding techniques. One or more processors then enrich the user prompts by integrating contextual data from user interaction history and adapt the enriched user prompts to align with characteristics of Large Language Models (LLMs) and Application Programming Interfaces (API) requirements of the LLMs.