LLM Application Generation for Faster Telecom Service Development
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Solution Overview
Problem
Existing telecommunications systems face inefficiencies in application development, requiring extensive human input and network resource utilization, leading to prolonged development cycles and network congestion.
Innovation Solution
An application generation system utilizing a large language model (LLM) to predict outcomes and generate computer-readable instructions autonomously, reducing the need for human input and optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional human-driven application development is used, then application quality and customization can be maintained, but development time increases and network resources are consumed
Solution Approach 1:
The system enables self-service application development by allowing the telecommunications operator's network to automatically generate applications based on user profile data and service requirements, eliminating the need for manual programming and reducing development time while maintaining service quality
Solution Approach 2:
The system performs preliminary actions by pre-configuring application parameters, selecting appropriate network resources, and preparing service templates before actual application deployment, thereby accelerating the development cycle and reducing time-to-market
2Ease of manufacture
If extensive human input and manual development are used, then application complexity can be managed, but network traffic increases and bandwidth is consumed
Solution Approach 1:
The network performs self-service by automatically generating applications and configuring service parameters based on stored user profiles and service templates, eliminating the need for extensive manual configuration traffic and reducing network resource consumption
Solution Approach 2:
The system uses copying by replicating proven service templates and configurations to create new applications, rather than building from scratch. This reduces the amount of data that needs to be transmitted and processed, thereby reducing network traffic and energy consumption
Data Source
AI summary
Techniques for automating generation of an application using a generative machine learned model are described herein. A telecommunications system can implement an application generation system for receiving an inquiry and/or question associated with bringing an application to market including generating computer readable instructions using one or more generative machine learned models. The techniques can also or instead include determining a level of risk, an overall cost, and/or feasibility for generating the application. The application generation system can implement a large language model (LLM) that predicts outcomes for various stages of a software development life cycle to improve timing, overall quality, and reliability to develop the application.