Context-Aware Authoring Prompts for LLM Personalization
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Solution Overview
Problem
Existing language models, such as large language models (LLMs), struggle to provide personalized responses for specific users or contexts without extensive training, leading to generalized outputs that do not account for individual user preferences or intended audiences.
Innovation Solution
A system that generates personalized responses by analyzing user and context data to create pseudo-documents, which are integrated into prompts for LLMs, enabling customized content creation without additional training resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If language models are trained to provide customized responses to individual users, then the personalization and relevance of responses is improved, but the computational resources and training time required increases significantly
Solution Approach 1:
The system segments the personalization process into two distinct components: (1) a lightweight user profile extraction module that collects and structures user data without extensive training, and (2) a prompt engineering module that injects this structured data into the language model's input. This segmentation allows personalization without requiring the language model itself to be retrained, thereby reducing computational resources while maintaining adaptability.
Solution Approach 2:
The patent introduces an intermediary layer between the user data and the language model. This intermediary consists of structured prompt templates that translate raw user data into formatted context strings. The intermediary handles the adaptation logic externally, allowing the language model to remain unchanged and avoiding the need for extensive retraining, thus resolving the contradiction between personalization and computational cost.
2Productivity
If language models provide generalized responses based on training data, then the computational overhead is reduced, but the relevance and completeness of responses to specific user contexts deteriorates
Solution Approach 1:
The system performs preliminary action by pre-collecting and structuring user data into profiles before the actual response generation. User attributes, preferences, and contextual information are organized in advance into standardized formats that can be quickly injected into prompts. This preliminary preparation enables the language model to generate relevant responses without requiring extensive processing during the interaction, thus maintaining productivity while improving contextual relevance.
Solution Approach 2:
The patent changes the parameters of the input prompt dynamically based on user profiles. Instead of using a fixed prompt structure, the system modifies prompt parameters (such as user context, preferences, and situational data) to match each user's specific needs. This parameter adaptation allows the language model to maintain high efficiency while generating contextually relevant responses, as the model processes varied but structured input rather than requiring retraining.
3Measurement precision
If user-specific data is collected and analyzed to create personalized prompts, then the accuracy and completeness of responses is improved, but the system complexity and data processing requirements increases
Solution Approach 1:
The system applies local quality by collecting and processing only the specific user data relevant to each interaction context, rather than analyzing all possible user attributes uniformly. The prompt structure includes only the necessary user profile elements needed for the current task, allowing high response accuracy while avoiding the complexity of processing and storing unnecessary data. This selective data handling reduces system complexity while maintaining precision.
Solution Approach 2:
The patent uses copying by creating simplified representations (copies) of user data in the form of structured profiles and prompt templates. Instead of directly processing complex raw user data during interactions, the system uses pre-created profile copies that contain essential information in a standardized format. This copying approach maintains response accuracy while significantly reducing the computational complexity of real-time data processing.
Data Source
AI summary
A method for automatically generating content for a user based on context results in personalized authoring assistance that can be provided by an already trained language model without the need for additional training. The method includes receiving a user query including a context, conducting a search of user data to generate first search results, applying one or more first models to the first search results to infer first patterns associated with the user and to generate a first set of content based on the first patterns, applying one or more second models to context data to infer second patterns associated with the context and to generate a second set of content based on the second patterns, and generating a pseudo-document. The method further includes transmitting the prompt to the language model to generate a response that is customized to the user and the context.