LLM File Vectorization for Personalized User Onboarding
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Solution Overview
Problem
Conventional onboarding computing systems fail to efficiently identify user needs and provide personalized onboarding sessions, leading to user resistance and abandonment, especially when transitioning from less technical platforms to advanced systems like QuickBooks Online.
Innovation Solution
A system that extracts data from user files using parser plugins, transforms it into a vectorized format, and uses a large language model (LLM) to infer user characteristics, optimizing the onboarding session by mapping these characteristics to relevant system features and adapting components in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional onboarding systems present users with fixed lists of system features, then the system can provide comprehensive feature information, but page load times extend and user decision fatigue increases
Solution Approach 1:
The system performs preliminary actions by extracting data from user files before the onboarding session begins. Parser plugins process uploaded files to identify user characteristics, which are then used to pre-personalize the onboarding experience. This preliminary data extraction and processing enables the system to present only relevant features to each user, reducing page load times and decision fatigue while maintaining comprehensive information availability.
Solution Approach 2:
The onboarding system applies local quality by customizing the presentation of system features based on individual user characteristics. Instead of presenting a uniform fixed list to all users, the system adjusts the onboarding content, priority, and presentation style according to each user's specific needs, background, and file data, thereby optimizing both load time and information relevance for each individual.
2Adaptability or versatility
If conventional onboarding systems present users with extensive feature lists, then the system can cover all functionality, but user decision fatigue increases and abandonment likelihood rises
Solution Approach 1:
The system applies local quality by customizing the presentation of system features based on individual user characteristics. Instead of presenting a uniform fixed list to all users, the system adjusts the onboarding content, priority, and presentation style according to each user's specific needs, background, and file data, thereby optimizing both load time and information relevance for each individual.
Solution Approach 2:
The system extracts and focuses only on the most relevant system features for each user based on their uploaded files and inferred characteristics. By using parser plugins to analyze user data and identify key needs, the system extracts and presents only the essential features that matter to each user, filtering out irrelevant information and reducing decision fatigue while maintaining comprehensive functionality coverage.
3Productivity
If the system migrates user data from spreadsheets to the new system, then the new system can replace the old platform, but the complexity of data migration and system learning increases
Solution Approach 1:
The system performs preliminary actions by extracting data from user files before the onboarding session begins. Parser plugins process uploaded files to identify user characteristics, which are then used to pre-personalize the onboarding experience. This preliminary data extraction and processing enables the system to present only relevant features to each user, reducing page load times and decision fatigue while maintaining comprehensive information availability.
Solution Approach 2:
The system uses an intermediary approach by introducing parser plugins as specialized components that bridge the gap between diverse user file formats and the new system's data requirements. These plugins act as translators that convert various file formats into a unified internal representation, simplifying the data migration process and reducing the complexity of integrating user data from different platforms.
4Ease of operation
If the onboarding session is customized to each user's needs, then user engagement improves, but the system complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary actions by extracting data from user files before the onboarding session begins. Parser plugins process uploaded files to identify user characteristics, which are then used to pre-personalize the onboarding experience. This preliminary data extraction and processing enables the system to present only relevant features to each user, reducing page load times and decision fatigue while maintaining comprehensive information availability.
Solution Approach 2:
The system applies self-service by using parser plugins to automatically extract and analyze user data from uploaded files without requiring manual input or complex configuration. The system autonomously processes file data, infers user characteristics, and generates personalized onboarding content automatically, reducing the burden on both users and system administrators while maintaining high engagement levels.
Data Source
AI summary
Systems and methods for adapting an onboarding session to a user are disclosed. An example method is performed by one or more processors of a system and includes receiving a transmission over a communications network from a computing device associated with a user of the onboarding system, the transmission including one or more files, extracting data from each of the one or more files using one or more parser plugins, transforming the extracted data into a set of arrays, feeding a prompt including the set of arrays to a large language model (LLM), inferring characteristics of the user based on a response to the prompt from the LLM, mapping the inferred characteristics to a predefined list of system features, and optimizing components of an onboarding session for the user based on the mapping.