LLM-Generated Communication Flows From Natural-Language Requirements
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Solution Overview
Problem
Generating customized communication flows for different customer segments across various channels is resource-intensive and time-consuming for enterprises, requiring explicit programming efforts.
Innovation Solution
Utilizing a large language model with a user interface to automatically generate and refine communication flows by providing well-informed input, selecting appropriate templates, and post-processing the output to ensure accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a programmer explicitly generates communication flows for each customer segment and channel, then the communication strategy is customized and accurate, but the time and resources required increase significantly
Solution Approach 1:
The system enables non-programmer users to generate communication flows themselves through an automated interface that uses large language models. Users input their requirements in natural language, and the system automatically generates the communication flow without requiring programmer intervention, thus reducing time loss while maintaining customization accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of programming communication flows with an automated system using large language models. The LLM processes natural language inputs and generates structured communication flows automatically, substituting the need for manual coding and reducing the time required while preserving customization capabilities.
2Manufacturing precision
If a programmer explicitly generates communication flows for each customer segment and channel, then the communication strategy is customized and accurate, but the resources and complexity required increase significantly
Solution Approach 1:
The system empowers users to independently generate communication flows through a user-friendly interface that handles the complexity internally. Users simply provide their requirements in natural language without needing to understand the underlying system complexity, thus reducing perceived complexity while maintaining customization accuracy.
Solution Approach 2:
The large language model acts as an intermediary between the user's natural language input and the structured communication flow output. It handles the complex transformation process, translating user requirements into properly formatted communication flows without exposing the complexity to the user, thus reducing the perceived device complexity.
3Productivity
If automated generation using large language models is used, then the time and resources needed are reduced, but the accuracy and consistency of communication flows may be compromised
Solution Approach 1:
The system incorporates feedback mechanisms where the generated communication flows are validated against predefined criteria and standards. The large language model can be prompted with specific requirements and constraints, and the output is reviewed and refined iteratively to ensure accuracy and consistency, thus maintaining high precision while achieving fast automated generation.
Solution Approach 2:
The system performs preliminary actions by establishing clear guidelines, templates, and constraints before the generation process. By pre-defining the structure, tone, and requirements of communication flows, the large language model generates accurate and consistent results from the start, reducing the need for post-generation corrections and maintaining high precision.
4Ease of operation
If non-programmers are enabled to create communication flows, then accessibility and ease of operation improve, but the need for post-processing and validation increases
Solution Approach 1:
The system includes automated validation and feedback mechanisms that check generated communication flows for errors and inconsistencies. This feedback loop identifies issues that need post-processing, allowing the system to maintain ease of operation for non-programmers while minimizing the time required for validation through automated checking.
Solution Approach 2:
The system performs preliminary validation and structure enforcement during the generation process itself. By building validation rules and structural requirements into the generation phase, the need for extensive post-processing is reduced, thus maintaining ease of operation while minimizing additional time requirements.
Data Source
AI summary
A natural language description of a desired function to be achieved using an automated communication flow is received. A prompt template specifically for a communication channel is selected based on an analysis of the natural language description of the desired function to be achieved. A prompt for a large language model is automatically generated based on the natural language description, including by inserting at least a portion of the selected prompt template in the automatically generated prompt. The automatically generated prompt is provided to the pre-trained large language model. Based on an output of the large language model, an automated communication flow to be implemented for the communication channel is automatically generated.