AI-Enhanced Troubleshooting & RCA Acceleration

MEF Accelerator Silent Comet

Revolutionizing troubleshooting processes and expediting Root Cause Analysis (RCA) resolution without relying on specialized network expertise.

This Accelerator explores how to augment the output of LLMs with external knowledge using Retrieval Augmented Generation (RAG). This process includes collecting domain data, performing data preprocessing and transformation to vectors, and storing them in a vector database for efficient retrieval. We then enhance the model with domain-specific context sourced directly from these vectorized information stores, providing an additional layer of context to prompts beyond manual question fine-tuning.

We maintain flexibility by remaining open to leveraging Machine Learning (ML) techniques instead of LLMs, contingent upon the nature of the data available from domain providers. Each domain provider can aim to adapt Silent Comet’s approach based on the type of data they have. For unstructured data sources, LLMs can be used to extract insights and facilitate decision-making. Conversely, when dealing with structured datasets, ML algorithms can be employed to perform pattern recognition and predictive analytics. This dynamic approach ensures that we can effectively harness the power of both LLMs and ML to enhance troubleshooting and Root Cause Analysis (RCA) processes across diverse network environments.