AI Model Copilot for Context-Rich Debugging and Drift Analysis
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Solution Overview
Problem
Conventional AI model building, debugging, and deployment platforms lack advanced analysis capabilities, providing only pre-programmed checks and limited functionality, failing to offer insights and suggestions for improvement.
Innovation Solution
An AI assistant leverages large language models (LLMs) to generate insights by wrapping user questions with AI model statistics, development platform data, and guidelines, enabling deeper cognitive analysis and performance tracking to assist in building, debugging, and deploying AI models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional platforms provide pre-programmed checks and pre-programmed outputs, then the platform operation is simple and reliable, but the functionality is limited and cannot provide advanced analysis
Solution Approach 1:
The patent introduces an AI assistant as an intermediary layer between the developer and the AI model. This assistant leverages LLMs to provide intelligent insights, suggestions, and debugging support, thereby enhancing functionality without requiring the entire platform to become significantly more complex. The AI assistant acts as a mediator that translates complex model states into actionable information for developers.
Solution Approach 2:
The AI assistant enables the platform to provide self-service through automated analysis. By using LLMs to automatically generate insights, detect issues, and suggest improvements based on model performance data, the system reduces the need for manual platform configuration and complex user intervention, thereby improving functionality while keeping the user interface simple.
2Loss of information
If AI models are tracked over time for performance analysis, then deeper insights can be generated through cognitive analysis, but the system complexity increases
Solution Approach 1:
The system performs preliminary tracking and collection of model performance data over time, storing it in a structured manner. This pre-prepared data is then fed to the AI assistant when insights are needed, allowing deep cognitive analysis without requiring complex real-time processing systems. The preliminary action of data collection and structuring simplifies the overall system architecture while enabling deep insights.
Solution Approach 2:
The AI assistant serves as an intermediary that processes tracked performance data and generates insightful analyses. By separating the data collection/tracking function from the analysis function, the system can maintain simple tracking mechanisms while using the AI assistant's cognitive capabilities to generate deep insights from the accumulated data.
3Measurement precision
If the AI assistant wraps user questions with AI model statistics and guidelines, then the LLM can generate more accurate and insightful responses, but the processing time increases
Solution Approach 1:
The system performs preliminary wrapping of user questions with relevant AI model statistics, context, and guidelines before sending to the LLM. This pre-processing step ensures that the LLM receives well-structured, context-rich inputs that improve response accuracy. By preparing the input format in advance, the system optimizes the overall processing efficiency while maintaining high response quality.
Solution Approach 2:
The AI assistant selectively wraps only the necessary portions of information (local quality) relevant to the specific user question and model state. Rather than always including all possible data, the system identifies and includes only the most relevant statistics, context, and guidelines for each specific inquiry, thereby reducing unnecessary processing time while maintaining response accuracy.
Data Source
AI summary
Artificial Intelligence (AI) may be used to generate insights to assist in building, debugging, and deploying AI models. In one or more embodiments, a user (e.g., AI developer) may pose a question (e.g., in a user interface), and a router and planner may wrap the question with AI model data and guidelines to send to an LLM. Based on the complex input—i.e., not just the user-posed question—the LLM based answer may provide additional insights and information that may not be readily apparent to the user. In one or more embodiments, the performance (e.g., drift) of the AI model may be tracked over time, and such tracking may be used to generate deeper answers through cognitive analysis. The answers may be displayed back on the user interface. Therefore, the embodiments herein can be leveraged as a co-pilot when developing AI models.