AI & Automation

Introducing RAG: How Can Retrieval Augmented Generation Fine-Tune your LLM

The Conversational Network: AI-Powered Language Models for Smarter Cable Operations          

  • How can GenAI help operators and vendors all be more efficient and effective
  • Exploring the potential of GenAI and LLMs to solve operational challenges:
    • Assisting technicians and engineers
    • Improving on the job training
    • Writing technical documents, standards, and specifications
  • Lesson learned: Challenges, tactics, tools, information sharing and innovation.

Tyler Glenn
Principal Engineer

Supercharging Proactive Network Maintenance by Leveraging Generative AI and ML       

  • Obtaining insights to develop tools and best practices for deploying AI/ML for DOCSIS technologies 
  • Developing methods to to fine-tune and refine the performance of LLMs on domain-specific data: Retrieval Augmented Generation (RAG) and low rank adaption (LoRA)
  • Building tools and sharing best practices for deploying AI/ML technologies
  • Developing frameworks objective measures using NLP-based metrics to measure, track, and improve model performance and efficacy

Santhana Chari
VP, Broadband Analytics and Data Science 

Leveraging JSON Data for a Network Chatbox  

  • Understanding the constraints; identifying corporate requirements and needs to further develop large language models (LLMs)
  • Implementing a retrieval augmented generation (RAG) framework based on the LangChain orchestrator that runs an LLM agent
  • Retrieving relevant network data in JSON snippets from a knowledge graph database 
  • Developing wider strategies to meet the requirements and needs in network data chatbots

David Suh
Lead Software Engineer
Cox Communications

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