Auto-Prompt Engine for Contextual Network Management Tasks
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Solution Overview
Problem
Existing network management systems face challenges in generating effective prompts for Large Language Models (LLMs) due to the integration of network domain knowledge, lack of automatic prompt generation, and lack of multi-task prompts, leading to inefficiencies in network automation and human error.
Innovation Solution
A unified prompt-based network management system with an intelligent auto-prompt engine that automatically generates contextualized prompts using network domain knowledge and user information, incorporating reverse inferences to adapt to different application scenarios and tasks, thereby enhancing the accuracy of LLM-generated solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual prompt engineering is used for LLMs in network management, then prompt accuracy can be improved, but time consumption and human error increase
Solution Approach 1:
The system enables automatic prompt generation where the network management system itself creates prompts based on input data, network knowledge information, and reverse inferences from AI models, eliminating the need for manual prompt engineering while maintaining high accuracy
Solution Approach 2:
The system performs preliminary processing by generating contextualized prompts before they are used for LLM inference, preparing all necessary components including network context, user information, and reverse inferences in advance to eliminate manual intervention
2Productivity
If generic prompts are used for LLMs, then prompt generation speed increases, but solution accuracy for specific network tasks decreases
Solution Approach 1:
The system generates prompts with local quality by customizing each prompt according to specific network context, user information, and task requirements rather than using generic prompts, ensuring high accuracy for each specific network management task
Solution Approach 2:
The system changes prompt parameters dynamically by adjusting the prompt content based on input data, network knowledge information, and reverse inferences, allowing the same base prompt structure to adapt to different network management scenarios with high accuracy
3Measurement precision
If network domain knowledge is integrated into prompts, then solution accuracy improves, but device complexity increases
Solution Approach 1:
The system segments the complex task of prompt generation into distinct components: input data processing, network knowledge information retrieval, reverse inference generation, and prompt assembly, making the overall complex system manageable through modular segmentation
Solution Approach 2:
The system uses an intermediary prompt generation module that mediates between the raw input data and the final LLM prompts, processing and transforming information through intermediate steps to integrate network domain knowledge without directly exposing system complexity
4Productivity
If automatic prompt generation is implemented, then productivity increases, but prompt accuracy may decrease due to lack of human review
Solution Approach 1:
The system incorporates feedback mechanisms where reverse inferences from AI models are used to generate and refine prompts, creating a feedback loop that continuously improves prompt accuracy while maintaining automatic generation, ensuring reliable outputs without human review
Data Source
AI summary
A unified prompt-based network management system that involves an intelligent auto-prompt engine generating contextualized prompts for an artificial intelligence model. The artificial intelligence model generates instructions and/or solutions and adapts to different application scenarios based on an enterprise network knowledge and reverse inference(s). Specifically, methods are provided that involve obtaining input data related to a configuration or an operation of one or more assets in an enterprise network and generating a contextualized prompt based on the input data, network knowledge information of the enterprise network, and at least one reverse inference generated using an artificial intelligence model. The methods further involve providing the contextualized prompt to the artificial intelligence model for generating a tailored response to the input data, wherein the tailored response includes a set of actionable tasks to be performed with respect to the one or more assets of the enterprise network.