Generative AI Response Modules for Tailored User Queries
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Solution Overview
Problem
Existing generative AI systems fail to effectively tailor responses to specific users and use cases, relying on a single language model and limited recommendation engines that cannot generate new content.
Innovation Solution
The use of response modules comprising a foundational generative language model, secondary models such as retrieval and adapter models, and prompting optimization models, along with configuration files and user data, to tailor responses to specific users, user segments, and use cases by modifying inputs and adapting outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single language model is used to generate responses, then the system is simple and easy to operate, but it fails to effectively tailor responses to specific users and use cases
Solution Approach 1:
The system segments the response generation process into multiple specialized models: a foundational language model for general responses, domain-specific models for specialized knowledge, and user preference models for personalization. Each model handles specific aspects of the response generation, allowing the system to tailor responses to different users and use cases while maintaining manageable complexity through modular architecture
Solution Approach 2:
The system creates a universal response generation framework that can handle multiple functions through a single integrated system. The framework incorporates various models that can be selectively activated based on the user and use case, making the system adaptable to diverse scenarios without requiring separate systems for each function
2Adaptability or versatility
If recommendation engines are used to personalize content, then user preferences can be accommodated, but the system is limited to a finite pool of preexisting content and cannot generate new content
Solution Approach 1:
The system merges the capabilities of recommendation engines with generative AI models. It combines user preference analysis (from recommendation systems) with content generation (from language models) into a unified framework that can both understand user preferences and create new content tailored to those preferences, eliminating the limitation of relying solely on preexisting content pools
3Manufacturing precision
If multiple machine learning models are used in response modules, then high-quality tailored responses can be generated, but the system complexity increases
Solution Approach 1:
The system implements dynamic model selection and configuration where the architecture adapts based on the specific user and use case requirements. Different combinations of models are activated dynamically rather than always using the full complex architecture, allowing high-quality responses when needed while reducing complexity for simpler cases through adaptive configuration
Data Source
AI summary
Exemplary systems and methods are provided for generating natural language responses to input queries by receiving a first input query; determining one or more of characteristics of the input query; selecting, from a plurality of response modules, one or more response modules based on the one or more characteristics of the input query and one or more metrics associated with each of the one or more response modules, wherein each response module of the plurality of response modules comprises a plurality of machine learning models for generating a response to the input query; and generating one or more responses to the first input query using the selected one or more response modules.