Persona-Based Response Reformatting for Query-System LLMs
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Solution Overview
Problem
Existing large language models (LLMs) provide answers that are not formatted according to user expertise or preferences, leading to dissatisfaction and suboptimal user experience.
Innovation Solution
A persona-based detection system that allows users to give real-time feedback on LLM responses, offering reformatting options tailored to individual user characteristics, including technical or non-technical preferences, through a reformatting module that adjusts content based on user personas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If LLM provides generic answers based on documentation, then information completeness is improved, but user satisfaction deteriorates due to mismatched format and expertise level
Solution Approach 1:
The system dynamically adapts the response format, detail level, and language complexity based on detected user personas and real-time feedback. The LLM transforms generic documentation-based answers into personalized responses that maintain information completeness while adjusting presentation to match user expertise levels and preferences.
Solution Approach 2:
The system changes multiple parameters of the LLM output including format type (code, text, table), detail level, language complexity, and technical jargon usage based on user persona detection. These parameter adjustments resolve the contradiction by maintaining full information while adapting presentation to user needs.
2Reliability
If LLM output follows documentation structure, then information accuracy is improved, but adaptability to user preferences deteriorates
Solution Approach 1:
The system segments the LLM response into multiple format options (code blocks, text explanations, tables, bullet points) that can be independently selected based on user preference. This segmentation allows the core accurate information to remain intact while adapting the presentation structure to user needs.
Solution Approach 2:
The system provides multiple output format options for the same underlying information, making the LLM universally adaptable to different user preferences while maintaining information accuracy. The same core content can be presented as code, text, tables, or other formats based on user persona.
3Ease of operation
If real-time feedback options are added for reformatting, then user satisfaction is improved, but system complexity increases
Solution Approach 1:
The system implements real-time feedback mechanisms where users can indicate preferences for different response formats. This feedback loop enables the LLM to adjust subsequent responses to match user preferences, improving satisfaction while managing complexity through iterative adaptation rather than complex upfront configuration.
Solution Approach 2:
The system uses user feedback to automatically adjust its own output format without requiring complex external configuration. The LLM serves itself by learning from user responses and autonomously adapting its presentation style, reducing the need for complex system-level complexity management.
Data Source
AI summary
A system and method are provided for personalizing large language models in query systems.