Custom AI Agent Actions With Context Variables for Data Analysis
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Solution Overview
Problem
Generative AI systems struggle with up-to-date information, factual accuracy, and consistency due to reliance on static, pre-trained knowledge, leading to inefficient computational resource usage and exposure of sensitive data.
Innovation Solution
A structured approach integrating generative AI with data analytics GUIs, allowing custom action administrators to define prompts with context variables, reducing redundant queries and maintaining relevance and accuracy by using metadata, thus optimizing computational resources and ensuring data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generative AI systems rely on static, pre-trained knowledge, then they can operate independently, but they suffer from outdated information, factual inaccuracies, and consistency issues
Solution Approach 1:
The patent introduces an intermediary layer between the generative AI system and the data source. This intermediary retrieves real-time data from external sources and injects it into the AI's context window, allowing the system to maintain factual accuracy without requiring complex retraining pipelines or direct connections to data sources.
Solution Approach 2:
The system performs preliminary data retrieval and context preparation before generating AI responses. By pre-fetching relevant data and structuring it appropriately, the system ensures factual accuracy is maintained without adding complexity to the core AI architecture during runtime.
2Reliability
If generative AI systems access external data sources frequently, then they maintain up-to-date information, but computational resource consumption increases
Solution Approach 1:
Instead of retrieving all available data or continuously querying external sources, the system performs partial data retrieval only when necessary to answer specific user questions. This selective approach maintains information currency while significantly reducing computational overhead compared to continuous or comprehensive data access.
3Productivity
If generative AI systems process large amounts of data, then they provide comprehensive analysis, but data privacy risks increase
Solution Approach 1:
The system extracts only the specific data elements needed to answer user questions rather than processing entire datasets. By taking out only the necessary portions of data, the system maintains comprehensive analysis capabilities for specific queries while minimizing privacy exposure by not unnecessarily processing sensitive information.
Data Source
AI summary
Techniques for creating and executing custom AI-driven actions in a data analysis interface are disclosed. One or more embodiments receive user input that defines a custom action by specifying an AI prompt with a natural language query and context variables. This definition is stored for later use. One or more embodiments present a graphical user interface (GUI) displaying data items, allowing users to select a subset. Based on the stored definition of a custom action, an option to execute the custom action appears. When the option is chosen, one or more embodiments assign metadata values from the selected items to the context variables, creating initialized context variables. The complete prompt, including the query and initialized variables, is submitted to an AI agent. The AI's response is displayed in the GUI, providing context-aware insights within the data analysis workflow.