Foundation Model Integration for Personalized Software Onboarding
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Solution Overview
Problem
New users face challenges in effectively onboarding with software applications due to barriers such as understanding the suitability of software, navigating application interfaces, and varying technical knowledge levels, leading to potential loss of customer engagement and missed opportunities for skill development.
Innovation Solution
A system that utilizes a computing apparatus to receive natural language input from users, generate prompts for a foundation model service using AI-engineered targeted and global portions, and displays recommended tasks and applications in a user interface, tailored to the user's skill level and subscription status, to facilitate personalized engagement and task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional documentation (quick-start guides, user manuals) is provided to new users, then users have access to information about the application, but users find the documentation tedious, time-consuming, and difficult to follow
Solution Approach 1:
An AI assistant is introduced as an intermediary between the user and the application documentation. The AI assistant processes user queries and provides targeted, context-aware guidance, eliminating the need for users to navigate through comprehensive but tedious manuals. The AI acts as a mediator that translates user needs into relevant information from the application's knowledge base.
Solution Approach 2:
The traditional mechanical system of users manually searching through static documentation is replaced with an intelligent AI-based system. Instead of users physically navigating through manuals, the AI assistant automatically understands user intent and retrieves relevant information, substituting the manual search mechanism with an intelligent query-response system.
2Adaptability or versatility
If comprehensive documentation is provided to all users, then all users have access to information, but the documentation is not sufficiently responsive to individual user's task or skill level
Solution Approach 1:
The documentation system transitions from providing uniform information to all users to providing localized, tailored information based on each user's specific context, task, and skill level. The AI assistant analyzes the individual user's needs and delivers customized guidance, making different parts of the interaction adaptive to the specific user rather than applying a one-size-fits-all approach.
Solution Approach 2:
The documentation system becomes dynamic and adaptive rather than static. The AI assistant continuously adjusts its responses based on user feedback, conversation history, and detected skill level, making the information delivery flexible and responsive to changing user needs throughout the interaction.
3Adaptability or versatility
If users with varying technical knowledge levels use the same application interface, then the application serves diverse user bases, but users lack appropriate guidance tailored to their skill level
Solution Approach 1:
The system changes the parameter of information delivery based on detected user skill level. The AI assistant adjusts the complexity, terminology, and detail of its responses according to the user's technical knowledge, transforming a static documentation approach into a dynamic one that adapts to user parameters such as expertise level and task complexity.
4Loss of information
If users rely on community forums for help, then users can access peer support, but the community may have incomplete information about application capabilities
Solution Approach 1:
The AI assistant creates an accurate copy or representation of the complete application knowledge base and capabilities, making this authoritative information directly accessible to users. Instead of users searching through potentially incomplete community discussions, the AI provides information that accurately reflects the application's actual capabilities, copied from the official documentation and knowledge sources.
Data Source
AI summary
Technology is disclosed herein for a foundation model service integration for personalized engagement with respect to an application service. In an implementation, a computing apparatus receives natural language input from a user relating to a job to be performed by one or more applications. The computing apparatus receives a targeted portion generated by an AI engine which includes information relating to the user with respect to the one or more applications. The computing apparatus generates a prompt for a foundation model service including a global portion and a targeted portion. The prompt tasks the foundation model service with identifying one or more tasks relating to the job and identifying a recommended application in association with the task. The computing apparatus enables display of the one or more task and the one or more recommended applications from the foundation model service in a user interface.