Structured Prompt Builder for Accurate, Lower-Effort LLM Queries
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Solution Overview
Problem
Existing Large Language Models (LLMs) face limitations such as training data cut-off dates, real-time data retrieval issues, and biases, leading to inadequate responses for time-sensitive queries and requiring users to craft detailed and context-rich prompts that they often struggle to provide.
Innovation Solution
A structured prompt builder system that dynamically guides users through refining their queries into detailed prompts using interactive intent indicators and real-time data retrieval, enhancing user interaction with LLMs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users craft detailed and context-rich prompts manually, then response accuracy improves, but user cognitive load and time investment increase
Solution Approach 1:
The patent introduces an intermediary system (prompt generation interface with intent indicators) that mediates between the user's simple query and the LLM's requirement for detailed prompts. The system automatically expands queries into structured prompts with relevant context, maintaining response accuracy while eliminating the need for users to manually craft complex prompts.
Solution Approach 2:
The system performs preliminary actions by automatically generating and refining prompts before they are submitted to the LLM. The prompt generation interface pre-processes user queries by adding context, structuring information, and selecting relevant intent indicators, so that when the LLM receives the prompt, it is already optimized for accurate response generation.
2Productivity
If LLMs are used for time-sensitive queries, then information processing capability improves, but response timeliness deteriorates due to training data cut-off dates
Solution Approach 1:
The patent implements dynamic prompt generation that adapts to time-sensitive contexts. The system dynamically selects and weights intent indicators based on the temporal nature of the query, prioritizing real-time information needs over static knowledge. This allows the LLM to process information efficiently while maintaining reliability for time-sensitive queries by adjusting the prompt structure to emphasize current relevance.
3Manufacturing precision
If structured prompt building is implemented, then prompt quality improves, but system complexity increases
Solution Approach 1:
The patent segments the prompt building process into discrete, manageable components represented by intent indicators. Each indicator corresponds to a specific aspect of the query (e.g., context, intent, constraints). This segmentation allows the system to generate high-quality structured prompts through modular selection and combination of indicators, rather than requiring complex monolithic prompt construction logic.
Data Source
AI summary
Described herein are systems and methods for enhancing user interaction with Large Language Models (LLMs) through structured prompt generation. A computer-implemented method and system for dynamically creating and refining user prompts based on user input, contextual data, and a hierarchical intent recommendation process are described. A structured prompt builder is provided, which guides users through the process of refining their initial query into a detailed prompt by presenting selectable intent indicators representing various facets or subtopics related to their query. The system includes an in-line word and phrase recommendation engine that suggests alternative words or phrases to refine the prompt further. The described techniques simplify the process of engaging with LLMs, democratizes access to advanced language model capabilities, and enhances the accuracy and relevance of LLM responses, thereby improving the overall search experience for users.