Dynamic Persona Generation Using Clustered LLM Workflow
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Solution Overview
Problem
Existing data management systems are fragmented, leading to inefficiencies in data analysis and hindered decision-making due to siloed user and operational data across multiple platforms, with manual persona generation being time-consuming and inaccurate, and large language models (LLMs) consuming excessive computing resources.
Innovation Solution
A persona-driven intelligence platform that automates dynamic persona generation using fine-tuned LLM agents, clustering data records, and generating personalized content for targeted digital communications, optimizing digital workflows by reducing computational resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual persona generation is used, then accuracy can be maintained, but time consumption increases significantly
Solution Approach 1:
The system performs preliminary clustering of data records into segments before persona generation, organizing data in advance to enable faster processing. This preliminary segmentation allows the LLM to generate personas more efficiently without sacrificing accuracy, as the data is already structured and ready for analysis.
Solution Approach 2:
The system generates template personas that can be reused and adapted across different campaigns and segments. These persona templates serve as reusable copies that capture common characteristics, reducing the time needed to create new personas while maintaining consistency and accuracy through proven patterns.
2Measurement precision
If standard LLMs are used for persona generation, then persona quality can be maintained, but computing resource consumption increases
Solution Approach 1:
The system segments the persona generation process into distinct stages: data clustering, feature extraction, and persona synthesis. By dividing the workflow and using only LLM capabilities where necessary rather than applying LLMs to entire datasets, computing resources are used more efficiently while maintaining persona quality through focused, targeted processing.
Solution Approach 2:
The system introduces intermediary processing layers between raw data and LLM input, including data clustering and feature extraction steps. These intermediaries prepare and filter data before LLM processing, reducing the computational burden on LLMs while preserving the quality of persona generation through careful data preparation.
3Reliability
If data is kept siloed across multiple platforms, then data security and organization can be maintained, but comprehensive insights and decision-making capability deteriorate
Solution Approach 1:
The system creates a universal persona framework that can integrate data from multiple siloed platforms while maintaining their organizational integrity. Personas serve as multi-functional objects that aggregate information across different data sources, enabling comprehensive insights without requiring physical consolidation of all data systems.
Data Source
AI summary
Methods and systems are provided for operating a persona-driven intelligence platform. Responsive to receiving inputs and/or instructions corresponding to a digital task, data records are retrieved from a database. The data records are clustered to generate a plurality of clusters of data records, based on a first set of attributes. A corresponding second set of attributes is obtained for each of the plurality of clusters, and a corresponding personality score vector is generated for each of the plurality of clusters, based on the second set of attributes. A dynamic persona corresponding to at least one of the plurality of clusters is generated by one or more large language model (LLM) agents and stored in a persona library. The disclosed methods and systems may enable improved real-time management of audience-driven digital workflows by automating unique persona generation, for enabling the optimization of digital tasks and data workflows to maximize relevancy.