Prompt Generation Engine for Structured AI Query Refinement
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Solution Overview
Problem
Conventional generative AI models lack guidance for users to structure their queries and information effectively, leading to unsatisfactory outputs due to incomplete or disorganized input prompts, which are often misinterpreted and result in low-quality responses.
Innovation Solution
A computer-implemented method that includes receiving an initial input, determining intent, providing a set of input requests, and applying a prompt generation engine to the intent, initial input, and subsequent inputs to generate a comprehensive input prompt for a machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional generative AI models are used with minimal user input, then the system operation is simple and quick, but the output quality and comprehensiveness deteriorate due to incomplete or misinterpreted input prompts
Solution Approach 1:
The system performs preliminary actions by proactively generating a structured prompt template based on the user's initial input before the user can provide complete information. This template includes placeholders for all necessary information categories, guiding the user to provide comprehensive input while maintaining quick response generation.
Solution Approach 2:
The system implements feedback by analyzing the user's initial input, determining what information is missing or insufficient, and using this analysis to generate targeted follow-up questions or prompt templates. This feedback loop ensures that the final prompt contains all necessary information for high-quality output while maintaining operational efficiency.
2Manufacturing precision
If the system requests multiple pieces of information from users to ensure comprehensive input prompts, then the output comprehensiveness improves, but the ease of operation deteriorates due to increased user burden
Solution Approach 1:
The system segments the information collection process by breaking down the comprehensive prompt into distinct categories or sections (e.g., project background, objectives, constraints, deliverables). Each segment is presented to the user separately with clear guidance, making the overall task more manageable while ensuring complete information collection.
Solution Approach 2:
The system acts as an intermediary by providing a structured prompt template that mediates between the user's initial input and the AI model's requirements. This template serves as an intermediate structure that organizes user thoughts and fills gaps automatically, reducing the user's cognitive load while ensuring comprehensive information provision.
3Manufacturing precision
If the system structure requirements for prompts are made strict to ensure accurate interpretation, then the manufacturing precision of output improves, but the device complexity increases due to additional processing layers
Solution Approach 1:
The system performs preliminary structuring by generating a pre-defined prompt template with clear sections and formatting requirements before passing the prompt to the AI model. This preliminary action ensures that the prompt meets structural requirements for accurate interpretation while keeping the processing complexity manageable through template reuse.
Solution Approach 2:
The system changes parameters by transforming unstructured or semi-structured user input into a standardized prompt format with specific structural parameters (e.g., required sections, formatting rules, length constraints). This parameter transformation ensures accurate interpretation by the AI model while the standardization reduces processing complexity through consistent formatting.
Data Source
AI summary
Techniques for improved prompt engineering are disclosed herein. An example computer-implemented method includes receiving an initial input from a user; determining an intent corresponding to the initial input; providing a set of input requests to the user based on the intent or the initial input; receiving one or more subsequent inputs from the user in response to the set of input requests; applying a prompt generation engine to (i) the intent, (ii) the initial input, and (iii) the one or more subsequent inputs to output an input prompt; transmitting the input prompt to a machine learning model that is configured to output a response to the input prompt; and causing the response to be displayed for viewing by the user.