Automated LLM Prompt Generation for Web Content
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Solution Overview
Problem
Generating appropriate prompts for large language models (LLMs) to produce context-specific text for websites is challenging, especially when done programmatically, as it requires understanding the webpage context, entity information, and tone, which is burdensome for entities and often orthogonal to their core competencies.
Innovation Solution
A website development system automates the generation of prompts by retrieving data from the website and using a prompt template with parameters, determining parameter values based on webpage data, and requesting user input for unknown values, to create a tailored prompt for the LLM to generate text for specific webpage sections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a user manually creates webpage text content, then the text can be customized to match brand tone and context, but it requires significant time and effort from users
Solution Approach 1:
The system performs preliminary actions by automatically retrieving entity information, webpage context, and metadata before text generation. This pre-processing of data eliminates the need for users to manually gather and prepare context information, significantly reducing time investment while maintaining customization quality
Solution Approach 2:
The patent introduces an intermediary system that acts as a bridge between the user and the LLM. This intermediary automatically generates tailored prompts by combining retrieved context with user preferences, then submits them to the LLM. This mediator handles the complex coordination work, freeing users from manual text creation tasks while preserving brand tone and context accuracy
2Reliability
If the system uses a generic prompt for LLM text generation, then the process is simple, but the generated text lacks context-specific relevance and brand alignment
Solution Approach 1:
The system applies local quality by customizing prompts specifically for each webpage section and entity context. Rather than using a single generic prompt, the system retrieves and incorporates entity-specific information, webpage metadata, and context-relevant data into tailored prompts. This ensures each text generation request is optimized for its specific context, improving accuracy and brand alignment
Solution Approach 2:
The system performs preliminary analysis of the webpage context, entity information, and desired text characteristics before constructing the prompt. This pre-processing step automatically gathers relevant data and structures it into a customized prompt format, reducing the apparent complexity for users while maintaining high text generation quality
3Ease of operation
If the system automatically retrieves all parameters for prompt generation, then user burden is reduced, but it increases system complexity and data processing requirements
Solution Approach 1:
The system implements self-service by automatically retrieving entity information, webpage context, metadata, and other necessary parameters without requiring manual user input. The system queries databases, analyzes webpage structure, and extracts relevant information autonomously, significantly reducing user burden while managing complexity through automated data processing pipelines
Data Source
AI summary
A website development system automatically generates text for a webpage. The system obtains a prompt template associated with a section of the webpage, where the prompt template includes one or more parameters. Based on the webpage, the prompt template determines a first value for a first one of the one or more parameters. A request to provide input for a second value of a second parameter is sent for display to a user. Using the prompt template, the first value, and the second value, the system generates a prompt to a large language model to generate text for the section of the webpage.