Prompt-Based LLM Probe Generation for Network Resource Discovery
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Solution Overview
Problem
Existing methods for discovering computing resources in IT infrastructure require manual hard-coding of probes, leading to inefficiencies and errors, especially when dealing with unsupported CI types, and are prone to failures due to configuration file changes.
Innovation Solution
A prompt-directed probe generator using a Large Language Model (LLM) automatically generates probes by parsing configuration files based on user inputs, eliminating the need for hard-coding and enhancing the discovery process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual hard-coding of probes is used for discovering computing resources, then discovery operations can be performed with existing supported CI types, but the process becomes inefficient and error-prone when dealing with unsupported CI types
Solution Approach 1:
The system enables automated self-service probe generation by using the LLM to automatically create probes from configuration files and natural language prompts, eliminating the need for manual hard-coding and enabling the system to handle unsupported CI types autonomously
Solution Approach 2:
The invention changes the fundamental parameter of probe creation from manual hard-coding to automated generation using large language models, transforming the process from a labor-intensive, error-prone activity to an efficient, reliable automated operation that can adapt to new CI types
2Adaptability or versatility
If manual hard-coding of probes is used, then existing supported CI types can be discovered, but the system lacks adaptability to unsupported CI types
Solution Approach 1:
The system performs preliminary action by pre-training the LLM on configuration files and probe patterns, enabling it to rapidly generate adaptive probes for new CI types without requiring time-consuming manual development and hard-coding processes
Solution Approach 2:
The invention creates a universal probe generation system that can handle both supported and unsupported CI types through a single LLM-based platform, eliminating the need for separate manual development processes for different CI type categories
3Reliability
If configuration files are manually parsed and probes are hard-coded, then discovery operations can proceed, but the process is prone to failures due to configuration file changes
Solution Approach 1:
The system introduces dynamics by enabling probes to be dynamically generated and updated based on current configuration files and user prompts, allowing the probe generation process to adapt automatically to configuration changes without requiring manual updates to hard-coded probe logic
Solution Approach 2:
The invention implements feedback mechanisms where the LLM analyzes configuration file changes and automatically adjusts probe generation accordingly, creating a closed-loop system that maintains reliability by continuously adapting to configuration variations
Data Source
AI summary
A method including parsing, via a Large Language Model (LLM), a configuration file comprising one or more attributes associated with one or more components of a network, wherein parsing the configuration file comprises generating a probe based on the one or more attributes. The method also includes executing the probe for discovery of one or more attributes associated with the one or more components of the network and updating a resource management database based on the one or more attributes.