Digital Assistant Prompt Optimization With User Feedback Control
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Solution Overview
Problem
Users lack the ability to accurately and efficiently adjust the prompts for digital assistants due to limited programming skills and understanding of underlying logic, leading to inefficient manual adjustments.
Innovation Solution
A user interface with a configuration region for receiving settings information and an optimization panel for automatic adjustments, allowing users to input and adjust settings information for a machine learning model to optimize digital assistant responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually adjust prompt settings for digital assistants, then customization capability is maintained, but adjustment efficiency and accuracy deteriorate due to limited programming skills and understanding of underlying logic
Solution Approach 1:
The system enables self-service by allowing the digital assistant to automatically adjust its own prompt settings based on user feedback and performance metrics. The assistant autonomously optimizes parameters such as temperature, top-p, and presence_penalty without requiring user intervention in the complex technical adjustments, thereby improving ease of operation while maintaining high adjustment efficiency through automated self-optimization algorithms
Solution Approach 2:
An intermediary optimization module is introduced between the user and the machine learning model parameters. This intermediary layer translates user-friendly feedback into precise parameter adjustments, shielding users from complex programming concepts while ensuring accurate and efficient prompt optimization. The intermediary handles the conversion of high-level user intentions into specific model parameter changes
2Productivity
If automatic optimization is implemented, then adjustment efficiency is improved, but user control and customization capability may deteriorate
Solution Approach 1:
The system implements dynamic control where the degree of automation adjusts based on user needs and context. Users can dynamically switch between fully manual, semi-automated, and fully automatic optimization modes. The system adapts its level of intervention based on the complexity of the task and user expertise, maintaining both high adjustment efficiency through automation and user customization capability through flexible control options
Solution Approach 2:
A multi-level feedback mechanism is established that allows users to provide feedback on automatic optimization results. The system continuously monitors user satisfaction and performance metrics, using this feedback to refine future automatic adjustments. Users can correct or override automated decisions, and the system learns from these corrections to improve both efficiency and adaptability over time
3Measurement precision
If complex parameter adjustments are made for optimal performance, then response accuracy is improved, but system complexity and difficulty of operation increase
Solution Approach 1:
The system extracts and isolates the complex parameter optimization logic into a separate automated module. Users interact only with simplified high-level controls while the extracted optimization engine handles complex parameter adjustments independently. This separation allows response accuracy to be improved through sophisticated parameter tuning without increasing the operational complexity visible to users
Solution Approach 2:
Manual mechanical adjustment of parameters is replaced with automated computational optimization. Instead of users directly manipulating complex parameters through interfaces, the system uses automated algorithms to compute and apply optimal parameter settings. This substitution maintains high response accuracy through precise parameter control while eliminating the complexity of manual parameter management
Data Source
AI summary
The embodiments of the invention provide a digital assistant creation method, apparatus, device, storage medium and program product. The method includes: presenting, in a user interface for creating a digital assistant, a configuration region configured to receive settings information for a digital assistant; presenting an optimization panel in response to detecting an automatic optimization indication of the settings information for the digital assistant; presenting, in the optimization panel, at least one adjusted portion of the settings information based on an adjustment indication received via the optimization panel and at least one portion of the settings information received in the configuration region; and determining, based on an acceptance indication or a rejection indication for the at least one adjusted portion of the settings information, the settings information to be presented in the configuration region.