DDI Expert Interface Using LLM Retrieval and Code Mediation
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Solution Overview
Problem
Existing solutions for network management, such as DNS, DHCP, and IPAM, require significant training and expertise, and existing LLMs struggle with accuracy and handling technical tasks like math, aggregations, and API requests, leading to inefficiencies and potential errors.
Innovation Solution
A linguistic operational expert system (Bloxy) that utilizes Large Language Models (LLMs) and vector embedding models to parse user queries, generate responses, and integrate retrieval augmented generation to provide a personalized, task-oriented user interface with interactive elements, reducing the need for extensive training and minimizing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing LLMs are used for network management tasks, then user accessibility is improved, but accuracy and reliability deteriorate due to struggles with math, aggregations, and API requests
Solution Approach 1:
The system segments the LLM's capabilities into two distinct components: a natural language processing front-end for user interaction and a structured code-generation back-end for executing precise network management tasks. This segmentation allows the LLM to handle user-friendly queries while delegating accuracy-critical operations to generated code, thereby maintaining both accessibility and reliability.
Solution Approach 2:
The system introduces an intermediary code-generation layer between the LLM and network management operations. Instead of the LLM directly performing math, aggregations, and API requests, it generates structured code that acts as a mediator to execute these tasks with precision, eliminating the LLM's weaknesses while preserving its natural language interface strengths.
2Reliability
If comprehensive training is provided for network management, then reliability is improved, but time consumption and complexity increase
Solution Approach 1:
The system enables self-service by allowing users to perform complex network management tasks through natural language queries without requiring extensive training. The LLM translates these queries into executable code automatically, making the system self-sufficient in bridging the gap between user intent and task execution, thereby eliminating the need for time-consuming training while maintaining reliability.
Solution Approach 2:
The system substitutes the mechanical training process with an automated code-generation mechanism. Instead of relying on users to learn and remember complex network management procedures through training, the system automatically generates the necessary code structures based on natural language input, replacing the training requirement with an automated translation process.
3Ease of operation
If LLMs are used for network management, then ease of operation is improved, but error rate increases due to inability to handle technical tasks accurately
Solution Approach 1:
The system inverts the traditional approach by not having the LLM directly execute tasks, but instead having it generate code that executes tasks. This inversion transforms the LLM's weakness in handling technical tasks into a strength, as the generated code can be validated, tested, and executed with precision, thereby reducing error rates while maintaining ease of operation.
Solution Approach 2:
The system creates a copy of the task execution process in the form of structured code generated by the LLM. This code copy serves as an intermediate representation that can be validated against expected outcomes before execution, allowing errors to be detected and corrected without affecting the user-friendly interface, thus reducing the error rate while preserving ease of operation.
Data Source
AI summary
Various techniques for building linguistic operational expert systems for DNS, DHCP, and IPAM (DDI) are disclosed. In some embodiments, a system/process/computer program product for building linguistic operational expert systems for DNS, DHCP, and IPAM (DDI) includes receiving a user request for DNS, DHCP, and IPAM (DDI) related information; processing the user request using a Large-Language Model (LLM); and generating a user interface (UI) output using the LLM and a vector embedding space, wherein the output includes generative UI content that was generated using the LLM and one or more relevant technical documentation for DDI related information that was selected based on a proximity to the user request in the vector embedding space.