LLM Network Troubleshooting With Expert-Curated Recipe Retrieval
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Solution Overview
Problem
Large language models (LLMs) struggle with complex network troubleshooting due to a lack of domain-specific knowledge and experience-based decision-making, despite advancements in prompting techniques like few-shot and chain-of-thought prompting.
Innovation Solution
Implementing expert-curated recipes within LLM-based network troubleshooting agents to provide contextual information and generate prompts for troubleshooting complex network issues, utilizing a recipe knowledge store and generation module to enhance troubleshooting capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If advanced prompting techniques (few-shot, chain-of-thought) are used to enhance LLM performance in multi-step tasks, then the model's ability to follow instructions and perform complex tasks improves, but the model still lacks domain-specific knowledge and experience-based decision-making capabilities required for complex network troubleshooting
Solution Approach 1:
The patent introduces an intermediary component (knowledge base or retrieval system) that bridges the gap between the LLM's general instruction-following capabilities and domain-specific troubleshooting knowledge. This intermediary retrieves relevant network troubleshooting knowledge, device configurations, and diagnostic information, then provides them to the LLM as contextual input, enabling the model to make reliable domain-specific decisions while maintaining its instruction-following strengths
Solution Approach 2:
The system performs preliminary retrieval and organization of domain-specific knowledge before the LLM engages in troubleshooting. By pre-fetching relevant network configurations, device specifications, and diagnostic procedures, the system ensures that the LLM has access to accurate domain knowledge upfront, rather than relying on its limited pre-trained knowledge
2Device complexity
If LLM-based agents are used for network troubleshooting without domain-specific knowledge integration, then the system complexity remains low and ease of deployment is high, but the troubleshooting accuracy and effectiveness deteriorate
Solution Approach 1:
The patent segments the troubleshooting system into distinct functional modules: a knowledge retrieval module that handles domain-specific information, an LLM module that processes instructions and generates responses, and an integration layer that combines them. This segmentation allows each component to be optimized independently while maintaining overall system simplicity and deployment ease
3Productivity
If standard LLM prompting techniques are used without domain knowledge integration, then implementation is simple and fast, but the ability to solve complex network issues deteriorates
Solution Approach 1:
The system implements self-service by automatically retrieving and integrating domain-specific knowledge without requiring manual configuration or fine-tuning of the LLM. The retrieval system autonomously queries knowledge bases, extracts relevant information, and feeds it to the LLM, maintaining implementation simplicity while dramatically improving adaptability to complex network scenarios
Data Source
AI summary
In one implementation, a device receives an input request for a large language model-based network troubleshooting agent regarding an issue in a network. The large language model-based network troubleshooting agent performs a lookup of a recipe based on the input request, wherein the recipe comprises contextual information for the issue. The device generates, by the large language model-based network troubleshooting agent, a prompt for a large language model based on the input request and on the recipe. The device provides, by the large language model-based network troubleshooting agent, the prompt to the large language model to troubleshoot the issue in the network.