Query Resolution Prompts for Domain-Specific AI Responses
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Solution Overview
Problem
Existing generative artificial intelligence frameworks lack the ability to incorporate specific context and purpose required for professional or domain-specific applications, leading to unreliable responses that do not consider regulatory requirements or application-specific workflows, and struggle to maintain consistency across different types of applications and users.
Innovation Solution
A query resolution model that uses customizable prompts and workflows tailored to specific organizational and user requirements, leveraging advanced machine learning techniques and algorithms to generate purpose-driven responses by parsing a series of prompts through an open artificial intelligence-based model, such as a Large Language Model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing generative AI frameworks are used for query resolution, then response generation is fast and generic, but the responses lack professional consistency and do not incorporate domain-specific context or regulatory requirements
Solution Approach 1:
The system segments the query resolution process into distinct components: a prompt generation module that creates domain-specific prompts, a query resolution model that processes these prompts, and a response generation component. This segmentation allows each component to specialize in specific tasks, improving professional consistency while managing complexity through modular design.
Solution Approach 2:
The patent introduces customizable prompts as intermediary elements between the user query and the query resolution model. These prompts incorporate domain-specific context, regulatory requirements, and professional guidelines, acting as a mediator that ensures the final response maintains professional consistency without requiring the entire system to be restructured.
2Measurement precision
If customizable prompts and workflows are implemented for each application field, then response accuracy and relevance improve, but the system complexity and customization overhead increase
Solution Approach 1:
The system implements a universal prompt template structure that can be adapted to different application fields through parameter customization rather than complete redesign. The same core prompt framework serves multiple domains (legal, medical, technical, etc.) by adjusting specific parameters such as domain context, regulatory requirements, and workflow steps, reducing customization overhead while maintaining response accuracy.
Solution Approach 2:
The patent utilizes parameter changes to adapt the prompt system to different application fields. By modifying parameters such as domain-specific context, regulatory constraints, and workflow configurations within a unified prompt structure, the system achieves high response accuracy across diverse domains without proportionally increasing system complexity.
3Reliability
If multiple parsing steps through the query resolution model are performed, then the response quality and alignment with organizational context improve, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-generating domain-specific prompts and incorporating organizational context, regulatory requirements, and domain knowledge into the prompt structure before the actual query resolution. This preliminary preparation ensures that the query resolution model has all necessary context upfront, reducing the need for multiple iterative parsing steps and thereby reducing processing time while maintaining high response quality.
Data Source
AI summary
Approaches for generating purpose driven responses to queries are described. According to one example, a query may be processed to determine an application field based on a context of the query and accordingly a set of customized prompts may be generated and then combined before being parsed through a query resolution model along with the query to generate a tailored response. The present invention enables tailored responses by leveraging multiple prompts to provide context and customization before querying the query resolution model.