Generative Prompt System for Link Note Generation
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Solution Overview
Problem
Users face challenges in obtaining additional information about web resources, as search results often provide limited information that may not align with the user's interests, leading to time-consuming reviews that yield insufficient results.
Innovation Solution
A computing system that generates prompts for users to provide link notes associated with web resources, using a generative model to process user data and content data to predict and display prompts, allowing users to input comments which are then stored and displayed alongside the web resource in search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If search results provide limited information, then search results are concise and easy to display, but users spend more time reviewing and obtaining insufficient information
Solution Approach 1:
The system performs preliminary action by proactively prompting users to provide link notes before they need to review the full web resource. The prompt generation occurs in advance based on content data and user data, preparing summarized information that users can quickly review without time-consuming full page inspections.
Solution Approach 2:
The system introduces an intermediary layer between search results and full web resources by generating and displaying link notes as intermediate summaries. These notes act as a mediator that provides relevant information extracted from web resources, allowing users to obtain sufficient information without directly reviewing the entire resource.
2Loss of information
If the system prompts users to provide link notes, then additional user-generated information is obtained, but the complexity of the system increases
Solution Approach 1:
The system employs self-service by using generative models to automatically generate prompts based on content data and user data. The prompts are dynamically created without manual intervention, and the system automatically collects, stores, and displays user-generated link notes, reducing the need for complex manual setup and maintenance.
Solution Approach 2:
The system applies parameter changes by dynamically adjusting prompt generation based on different user data parameters and content data characteristics. The generative model modifies prompt parameters according to user search history, browsing history, and the specific web resource being analyzed, allowing the system to adapt complexity levels to different contexts.
3Loss of information
If the system processes content data with generative models, then relevant information is extracted and summarized, but computational resources and processing time increase
Solution Approach 1:
The system applies partial action by processing only the necessary portions of content data through generative models rather than analyzing entire web resources. The system extracts and processes key information elements (content data and user data) needed for prompt generation, avoiding unnecessary computational overhead while maintaining information extraction accuracy.
Solution Approach 2:
The system performs preliminary processing by pre-processing content data and user data before generating prompts. This preliminary action involves organizing, indexing, and preparing data structures that can be quickly processed by generative models, reducing the computational burden during actual prompt generation and improving overall efficiency.
Data Source
AI summary
Systems and methods for generating prompts for user data entry can include obtaining context data. The context data can be processed to determine whether an input entry interface is to be provided. In response to determining an input entry interface is to be provided, the context data or other data associated with a content display instance can be processed with a generative model to generate a prompt that can be provided to the user. User input data can then be obtained and stored to be provided to other users.