Software Result Explanations with Generative AI Supplementation
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Solution Overview
Problem
Software applications provide explanations for results that lack sufficient detail for novice users, making them unhelpful.
Innovation Solution
Implement a generative artificial intelligence model to supplement explanations generated by software applications, allowing users to select from various modalities such as detailed explanations, follow-up questions, term definitions, and example scenarios to enhance understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the software application provides a standard explanation for a result, then the explanation is generated quickly and consistently, but the explanation lacks sufficient detail for novice users to understand
Solution Approach 1:
The explanation generation is segmented into multiple levels: a base explanation generated by the software application, and optional supplemental content generated by the generative AI model. Users can select specific modalities (e.g., detailed explanation, example scenarios, term definitions) to receive targeted supplemental information rather than a complete overhaul of the explanation system.
Solution Approach 2:
A generative artificial intelligence model is introduced as an intermediary between the software application's result determination and the user. This AI model receives the base explanation and user preferences, then generates supplemental content that bridges the gap between concise automated explanations and comprehensive novice-friendly guidance.
2Ease of operation
If the software application provides detailed explanations for all users, then novice users benefit from more information, but experienced users receive unnecessary detail that increases processing time
Solution Approach 1:
The explanation system becomes dynamic by allowing users to select their preferred modality and detail level. The generative AI model adapts the supplemental content based on user choices, providing detailed explanations only when needed rather than universally, thus optimizing the balance between understanding and time efficiency.
Solution Approach 2:
Different levels of explanation detail are provided to different users based on their needs. Rather than applying a uniform explanation level to all users, the system tailors the supplemental content quality locally to each user's selected modality and experience level.
3Adaptability or versatility
If multiple modalities for supplementing explanations are provided, then users can choose their preferred learning style, but the system complexity and development effort increase
Solution Approach 1:
The generative artificial intelligence model serves multiple functions across different modalities. Rather than implementing separate systems for each explanation type (detailed explanations, examples, definitions), a single AI model handles all modalities by receiving different prompts and generating appropriate supplemental content, thus achieving versatility without proportional complexity increase.
Data Source
AI summary
A method for generating supplemental content for an explanation for a particular result determined by a software application includes receiving data indicative of a user selecting a first modality of a plurality of different modalities for supplementing the explanation. In response to receiving the data, the method includes providing inputs to a generative artificial intelligence model. The inputs include data indicative of the explanation and data indicative of a first natural language prompt associated with the first modality. The method includes receiving an output from the generative artificial intelligence model. The output includes supplemental content for the explanation. The method includes displaying the supplemental content for viewing via a user interface.