# Automated Sports Commentary Generation at WSC Sport ## Company Overview WSC Sport is a technology company in the sports-tech industry that provides AI-based solutions for creating automated sports content. With 400 employees globally, they work with major sports organizations including the NFL, NHL, NBA, Bundesliga, and Premier League to generate automated real-time content for publishers. ## Problem Statement Traditional sports recap production faces several challenges: - Manual production takes 3-4 hours from data collection to script writing and studio recording - Need for real-time content delivery as games/events conclude - Requirement to produce content in multiple languages - Young audience preference for quick, engaging highlights with comprehensive commentary - Need for consistent quality and accuracy at scale ## Technical Solution Architecture ### Data Pipeline - Automated collection of game events and statistics - Structured data capture including: ### LLM Implementation The solution uses a three-component approach: 1. **System Prompts** - Context-aware prompting based on: 1. **Dynamic Prompt Generation** - Event-specific prompt construction - Attribute-based indexing system - Random sampling from relevant training examples - Semantic matching for cross-sport compatibility - Structured metadata templates 1. **Hallucination Prevention** - Chain-of-Thought (CoT) approach for fact verification - Breaking complex narratives into smaller, verifiable components - Iterative refinement process - Structured validation of generated content ### Anti-Hallucination Framework The system implements a specialized approach to prevent hallucinations: - Decomposition of complex scenarios into verifiable components - Explicit fact-checking against source data - Structured metadata validation - Iterative refinement through multiple LLM passes ### Workflow Integration 1. Event data collection and structuring 1. Dynamic prompt generation based on event context 1. LLM-based script generation with fact verification 1. Text-to-speech synthesis with emotion and timing control 1. Optional multilingual translation 1. Final output generation with synchronized video ## Key Technical Challenges Addressed ### Content Generation - Maintaining consistent narrative length - Ensuring video-commentary synchronization - Avoiding repetitive phrases and patterns - Managing context across different sports and leagues ### Language Model Control - Preventing factual hallucinations - Maintaining appropriate sentiment - Managing temporal consistency - Handling sport-specific terminology ### Multilingual Support - Accurate translation while maintaining context - Supporting diverse language requirements - Handling complex languages like Turkish and Polish - Maintaining consistent voice and style across languages ## Implementation Best Practices ### Focus Areas - Core script generation as the primary focus - Structured data handling - Robust validation pipelines - Clear separation of concerns ### Quality Control - Automated fact verification - Sentiment analysis - Timing and synchronization checks - Multi-stage validation process ### Scaling Considerations - Modular system design - Language-agnostic architecture - Efficient prompt management - Resource optimization ## Results and Impact The system successfully: - Reduced production time from 3-4 hours to 1-2 minutes - Enabled real-time multilingual content generation - Maintained high accuracy and engagement - Supported multiple sports and leagues - Enabled creative content formats (e.g., rap-style commentary) ## Future Developments The team continues to work on: - Enhanced graphics integration - More creative content formats - Expanded language support - Improved emotion and timing control - Additional sports coverage ## Technical Lessons Learned - Importance of structured data in prompt engineering - Value of breaking complex tasks into verifiable components - Need for sport-specific context in language models - Benefits of modular system design - Critical role of hallucination prevention in production systems