Structured Prompt Builder for Accurate, Low-Burden 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 inaccurate or nonsensical responses, especially when users fail to provide detailed and context-rich prompts, resulting in unsatisfactory interactions.
Innovation Solution
A structured prompt builder dynamically guides users to refine their queries into detailed prompts through an interactive interface with selectable intent indicators and real-time word/phrase suggestions, ensuring precise and contextually rich inputs for LLMs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users provide detailed and context-rich prompts, then response accuracy is improved, but cognitive load and interaction complexity increase
Solution Approach 1:
The patent introduces an intermediary system that includes intent detection modules, context extraction components, and prompt suggestion engines. These intermediaries automatically process user input, infer underlying intentions, and generate structured prompts with relevant context, thereby maintaining high response accuracy while shielding users from the cognitive burden of crafting detailed prompts manually
Solution Approach 2:
The system enables self-service by automatically enriching user inputs with contextual information, retrieving relevant data from knowledge bases, and generating optimized prompts without requiring user intervention. The system serves itself by autonomously performing the complex tasks of prompt engineering, context assembly, and intent refinement, thus improving accuracy without increasing user cognitive load
2Adaptability or versatility
If LLMs are used for complex queries, then response capability is improved, but reliability deteriorates due to training data cut-off and biases
Solution Approach 1:
The patent merges the capabilities of LLMs with traditional information retrieval systems, knowledge bases, and verification mechanisms. By combining the generative power of LLMs with the reliability of structured data sources and fact-checking algorithms, the system achieves both high response capability for complex queries and improved reliability through multiple layers of validation and cross-referencing
Solution Approach 2:
The system implements feedback loops where LLM-generated responses are automatically verified against knowledge bases, search results, and contextual constraints. The feedback mechanism detects inconsistencies, biases, or outdated information and triggers corrections or alternative response generation, thereby maintaining reliability while preserving the adaptability of LLMs for complex queries
3Ease of operation
If interactive prompt building is implemented, then user interaction quality is improved, but system complexity increases
Solution Approach 1:
The patent segments the prompt building process into distinct modular components: intent detection modules, context extraction components, suggestion generation engines, and prompt assembly units. Each module performs a specific function and can be independently developed, tested, and maintained. This segmentation improves user interaction quality through a structured interactive process while managing system complexity by organizing functionality into manageable, loosely-coupled modules
Data Source
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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.