Prompt Message Generation Using Rules and Example Retrieval
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Solution Overview
Problem
Manual creation and fine-tuning of prompts for Large Language Models is cumbersome and inefficient, especially when model updates occur, necessitating re-evaluation and frequent manual adjustments.
Innovation Solution
A method and device that automatically generate prompt messages by analyzing input content and predefined rules, retrieving example data, and synthesizing task prompts and examples to create optimized prompts for Large Language Models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual creation and fine-tuning of prompts is used, then prompt quality can be optimized, but development time and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary analysis of input content and predefined rules to generate task prompts and task examples before actual prompt message creation. This advance preparation automates the extraction of key information and formatting requirements, reducing the time needed for manual prompt engineering while maintaining quality through systematic processing
Solution Approach 2:
The system enables self-service by automatically generating prompt messages without requiring manual intervention for each prompt creation task. The automated generation process analyzes input content, retrieves relevant examples, and synthesizes prompt messages independently, eliminating the need for continuous manual fine-tuning and reducing operational complexity
2Manufacturing precision
If manual creation and fine-tuning of prompts is used, then prompt quality can be optimized, but operational complexity increases due to frequent re-evaluation and adjustment
Solution Approach 1:
The system incorporates feedback mechanisms by automatically retrieving task examples from databases and using them to refine and generate prompt messages. This automated feedback loop ensures prompt quality is maintained through systematic evaluation and adjustment rather than manual intervention, reducing operational complexity while preserving quality
Solution Approach 2:
The system performs self-service by automatically adapting to model updates and re-evaluating prompt performance without requiring manual intervention. The automated generation and retrieval processes handle re-evaluation independently, reducing the complexity of operational management while maintaining prompt quality through continuous automated optimization
3Productivity
If automated prompt generation is implemented, then development speed increases, but the level of automation needs to be balanced with quality control
Solution Approach 1:
The system uses predefined rules as an intermediary between automated processing and quality control. These rules guide the automated analysis of input content and generation of task prompts, ensuring that automation operates within structured parameters that maintain quality standards while enabling rapid development
Solution Approach 2:
The system retrieves and processes task examples from databases to generate prompt messages, using proven templates and patterns as copies. This approach enables rapid generation of high-quality prompts by adapting existing successful examples rather than creating everything from scratch, balancing automation speed with quality control through proven patterns
Data Source
AI summary
A non-transitory computer-readable storage medium storing one or more computer programs is disclosed. The one or more computer programs can be performed by one or more processors to perform: obtaining at least one key information based on an input content message and a predefined rule, and generating at least one task prompt according to the at least one key information; obtaining a format requirement information according to the predefined rule, and analyzing the input content message to obtain a semantic requirement information; retrieving at least one example data from a text example database according to the semantic requirement information, and processing the at least one example data according to the format requirement information to generate at least one task example; and synthesizing the input content message, the at least one task prompt and the at least one task example to generate a prompt message.