Contextualized Prompt Generation for Large Language Models
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Solution Overview
Problem
Large generative AI models (LXMs) face challenges in generating relevant and accurate responses due to the lack of contextual information, leading to suboptimal user experiences.
Innovation Solution
A method is introduced to generate contextualized prompts for LXMs by combining user prompts with relevant context information obtained from physical context sources and user background data, using relevance models and contextualized prompt generators to enhance the relevance and specificity of responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contextual information is added to prompts, then response relevance and accuracy improve, but prompt complexity and processing requirements increase
Solution Approach 1:
The patent segments the context information into different categories (user profile context, interaction context, environmental context) and selectively retrieves only the relevant portions for each prompt type. This segmentation allows the system to manage complexity by organizing information hierarchically and retrieving only what is needed, rather than processing all available context at once.
Solution Approach 2:
The patent introduces a context management system as an intermediary layer between the user's raw prompt and the LXM. This intermediary component handles the complexity of context retrieval, filtering, and integration, allowing the LXM to receive pre-processed contextualized prompts without directly managing the complexity of raw context data.
2Productivity
If more context information is retrieved and processed, then response relevance improves, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by retrieving and processing only the necessary context information relevant to the specific prompt type and user profile, rather than processing all available context. The system determines the appropriate level of context retrieval based on the prompt characteristics and user profile, avoiding unnecessary computational resources on irrelevant context data.
Solution Approach 2:
The patent dynamically adjusts context retrieval parameters based on user profile characteristics and prompt types. Different users and prompt scenarios trigger different levels and types of context retrieval, allowing the system to optimize computational resource usage by changing retrieval parameters adaptively rather than using a fixed approach.
3Adaptability or versatility
If user context information is collected and processed, then response personalization improves, but user privacy concerns increase
Solution Approach 1:
The patent extracts and processes only the necessary context information needed for personalized responses, separating useful data from unnecessary information. By selectively retrieving and processing only relevant context items based on prompt type and user profile, the system minimizes the amount of user data handled while maintaining personalization effectiveness.
Solution Approach 2:
The context management system serves as an intermediary that handles user data processing with privacy considerations. It processes context information locally and selectively, acting as a buffer between data collection and LXM processing, thereby reducing direct exposure of sensitive user information while maintaining personalization capabilities.
Data Source
AI summary
Various embodiments include systems and methods for generating a prompt for a large generative AI model (LXM). A computing device may be configured to receive a user prompt, obtain user context information from one or more sources of physical context information and user background information, use the received user prompt and the obtained user context information to generate a contextualized prompt for submission to an LXM, and submit the generated contextualized prompt to the LXM