LLM Prompt Grounding with Hierarchical Context for Accurate Responses
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Solution Overview
Problem
Administrative users face inefficiencies and increased costs in customizing large language model (LLM) prompts for different use cases, leading to redundant developmental efforts and excessive computational resource consumption.
Innovation Solution
A unified architecture for LLM prompt templates is provided, incorporating a client interface, augmentation service, and data transformers/refiners to standardize data processing across various use cases, ensuring accurate and reliable responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If administrative users customize LLM prompts for different use cases, then the responses can be more accurate and relevant to specific tasks, but the developmental efforts and costs increase significantly
Solution Approach 1:
The patent implements a unified prompt template architecture that serves multiple use cases through a single system. The framework allows different data sources and parameters to be configured within the same template structure, enabling one prompt template to handle various tasks such as data summarization, analysis, and generation across different domains without requiring separate customizations for each use case.
Solution Approach 2:
The patent segments the prompt template into distinct modular components including data source specifications, parameter definitions, and task-specific instructions. This segmentation allows administrative users to configure only the relevant portions for each use case while reusing the core template structure, thereby reducing developmental efforts while maintaining response accuracy.
2Manufacturing precision
If administrative users customize LLM prompts for different use cases, then task-specific accuracy improves, but computational resource consumption increases
Solution Approach 1:
By creating a universal prompt template framework that handles multiple use cases, the system avoids the computational overhead of maintaining and processing multiple separate customized prompts. The unified structure enables more efficient resource utilization while delivering task-specific accuracy through configurable parameters and data sources within the same template.
3Reliability
If multiple data sources are integrated into LLM prompts, then the reliability and accuracy of responses improve, but the complexity of data processing increases
Solution Approach 1:
The patent divides the data processing workflow into distinct modular stages including data retrieval from multiple sources, data transformation, data validation, and prompt assembly. Each stage handles specific aspects of multi-source data integration independently, reducing overall complexity while improving response reliability through systematic processing of diverse data inputs.
Solution Approach 2:
The unified prompt template acts as an intermediary layer between multiple data sources and the LLM. This mediator structure standardizes how data from different sources is formatted, validated, and integrated into the prompt, simplifying the complexity of multi-source data processing while ensuring reliable and consistent response generation.
4Productivity
If a unified architecture for LLM prompt templates is implemented, then scalability and efficiency improve, but the initial system complexity increases
Solution Approach 1:
The unified architecture implements a universal prompt template framework that can handle multiple use cases, data sources, and configurations through a single system structure. This universality improves scalability and efficiency by eliminating the need for multiple separate systems, while the modular design keeps the initial complexity manageable through clear separation of concerns and configurable components.
Data Source
AI summary
A computing system may receive, via a client interface, a query to trigger a prompt of a set of prompts configured for a large language model (LLM), where the query may be indicative of a set of data from one or more data sources linked to the prompt. The computing system may transmit, to an augmentation service, a request for a set of grounding data associated with the set of data from the data sources linked to the prompt. The computing system may then receive, from the augmentation service, the set of grounding data where the set of grounding data includes hierarchical context data from the data sources. The LLM may then be queried via the prompt using the first set of data and the set of grounding data. The response to the query may then be provided, to the client interface, for display via the client interface.