# Scaling LLM Applications in Telecommunications Industry This case study covers insights from a panel discussion featuring executives from Verizon, Anthropic, and Infosys about implementing LLM applications in the telecommunications industry, with particular focus on moving from proof of concept to production deployments. # Key Use Cases and Implementation Areas ## Content Generation and Knowledge Management - Processing and managing vendor documentation across 40,000+ landlords - Handling complex lease documentation with regulatory requirements - Improvement over traditional OCR approaches by using LLMs for information extraction - Enabling automation based on extracted information ## Software Development Lifecycle Enhancement - Transformation of requirements into prompts - Implementation of knowledge graphs to break down requirements into features - Automated generation of user stories - Integration of technical certification processes ## Co-pilot Applications - Focus on productivity enhancement rather than job replacement - Implementation of recommendation systems - Emphasis on user adoption and acceptance # Technical Architecture and Implementation ## Foundation Model Strategy - Implementation of wrapper architecture around foundation models - Model-agnostic approach to prevent vendor lock-in - Flexibility between on-premises and cloud deployment - Support for pay-per-call pricing models ## RAG Implementation Improvements - Use of contextual retrieval techniques - Focus on maintaining document context during chunking - Implementation of ranking systems for better retrieval accuracy - Addition of chunk descriptions to improve embedding quality # Evaluation and Quality Control ## Accuracy Requirements - Target of 95% accuracy for production deployment - Initial implementations often achieve 40-50% accuracy - Need for continuous improvement processes - Balance between accuracy and user adoption ## Evaluation Framework - Clear criteria for production readiness - Inclusion of accuracy benchmarks - Latency requirements - ROI modeling - Security testing (including jailbreak testing) - Regular re-evaluation as models evolve # Implementation Challenges and Solutions ## Cost and ROI Considerations - Typical AI product setup costs around $6 million - Need for 3-4X return over 3 years - Focus on problems with $40-50 million impact potential - Requirement for clear business value demonstration ## User Adoption - Initial resistance from end users - Impact of early accuracy issues on adoption - Need for high accuracy from first use - Importance of staged rollouts ## Data Management - Challenge of keeping information current - Need for regular updates to knowledge bases - Balance between automation and human verification - Integration of domain expertise # Best Practices and Lessons Learned ## Center of Excellence - Establishment of central governance - Implementation of tollgate processes - Technical feasibility assessment - Technology selection guidance ## Change Management - Education of senior leadership - Focus on productivity enhancement messaging - Integration with existing workflows - Continuous training and support ## Industry Collaboration - Potential for cross-operator data sharing - Focus on non-competitive areas - Opportunity for industry-wide standards - Balance between collaboration and competition # Future Directions ## Model Evolution - Trend toward larger context windows - Development of telecom-specific models - Integration with numerical data systems - Enhancement of prescriptive capabilities ## Infrastructure Development - Movement toward specialized language models - Integration with existing telecom systems - Development of domain-specific knowledge graphs - Enhancement of troubleshooting capabilities # Key Success Factors - Strong foundation in accuracy and reliability - Clear ROI justification - Effective change management - Robust evaluation frameworks - Continuous improvement processes - Focus on user adoption - Balance between automation and human oversight