AI Prompt Generation for Structured Message Delivery Queries
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Solution Overview
Problem
Existing virtual AI assistants generate unstructured, general responses that are not domain-specific and often fail to integrate effectively with SaaS platforms for message delivery management, leading to challenges in coordinating, executing, and troubleshooting messaging services.
Innovation Solution
A virtual AI assistant interface that receives unstructured queries, generates modified prompts including function identifiers and function definitions, and guides generative AI models to produce structured responses specific to the SaaS platform, integrating API calls to retrieve and display organization-specific message data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a virtual AI assistant generates responses using general NLP and machine learning algorithms, then the assistant can interact with users through text or voice interfaces, but the responses become unstructured and not domain-specific, failing to integrate effectively with SaaS platforms
Solution Approach 1:
The system performs preliminary action by defining function schemas and providing function definitions before the AI model generates responses. The prompt generation module creates modified prompts that include schema information and function definitions, guiding the model to produce structured responses that conform to required formats and integrate with SaaS platforms.
Solution Approach 2:
The patent introduces an intermediary mechanism - the prompt generation module - that sits between the user query and the AI model response. This intermediary transforms unstructured queries into modified prompts incorporating schema and function definitions, ensuring the output is structured and domain-specific rather than generic.
2Reliability
If the virtual AI assistant integrates with SaaS platforms for message delivery management, then effective coordination and troubleshooting can be achieved, but sensitive organization information may be exposed to external models
Solution Approach 1:
The system extracts sensitive organization-specific information from the prompt before it reaches the external AI model. By defining which information should be included in function calls and which should remain internal, the system separates sensitive data from the model processing, preventing exposure while maintaining integration effectiveness.
Solution Approach 2:
The organization's own data and function definitions serve the AI model rather than the other way around. The system uses the organization's message data and delivery management capabilities to enhance the AI's responses, making the organization's information the resource that serves the external model rather than being consumed by it.
3Ease of operation
If the AI model generates unstructured responses, then the assistant can provide general information, but the responses cannot be easily integrated with SaaS platform data and schemas
Solution Approach 1:
The system changes the parameter of response structure by modifying the prompt to include schema definitions and function signatures. This transforms the AI model's output from unstructured text to structured data that conforms to predefined formats, making it easily integrable with SaaS platform data while maintaining usability through natural language interfaces.
Data Source
AI summary
A software as a service (SaaS) platform receives a first indication identifying an initialization, at a user device, of a virtual artificial intelligence (AI) assistant for message delivery management at the SaaS platform. A user input identifying a query with first unstructured data is received via the virtual AI assistant and at the SaaS platform. A first prompt identifying the first unstructured data of the query and an identifier of a function related to the message delivery management at the SaaS platform is sent via a first application programming interface (API) call to a generative AI model. A first output of the generative AI model identifying first structured data related to the message delivery management based on the function is received.