This case study explores Vimeo's implementation of a production-grade video Q&A system that leverages modern LLM techniques to enable natural conversation with video content. The system represents a significant advancement in making video content more accessible and interactive through AI technology. The core challenge Vimeo faced was enabling users to extract value from video content without necessarily watching the entire video. Their solution combines several sophisticated LLMOps components working together in production: ## Core Architecture and RAG Implementation The system employs a bottom-up approach to transcript processing and storage: * Base level: Chunks of 100-200 words (1-2 minutes of video) * Middle level: 500-word chunks summarized to 100 words using LLMs * Top level: Full video description generated from all summaries This multi-level approach allows the system to handle both specific detailed questions and broader thematic queries effectively. All these representations are stored in a vector database, with each entry containing: * Text representation * Vector embedding * Original word timings * Timestamp ranges The RAG implementation is particularly noteworthy for how it handles the specialized domain of video content. Rather than treating the video as a single document, the system creates multiple representations at different granularities, allowing for more precise and relevant retrievals depending on the type of question being asked. ## Speaker Detection System One of the most innovative aspects of the implementation is the speaker detection system that operates without relying on facial recognition. The system uses a voting-based approach with multiple LLM prompts to identify speakers: * Analyzes conversation transitions for speaker identification * Uses multiple prompt types to generate "votes" for speaker identification * Implements confidence thresholds to prevent misidentification * Focuses on high-precision over recall (preferring no name over wrong name) This approach demonstrates sophisticated prompt engineering in production, using multiple prompt types to cross-validate results and ensure accuracy. ## Production Optimizations The system incorporates several practical optimizations for production use: * Separate prompting for answer generation and timestamp identification to improve performance * Vector similarity checks between chat answers and source content to verify relevance * Pregenerated questions and answers to improve initial user engagement * Dynamic generation of related questions based on context relevance ## Question-Answer System Design The Q&A component demonstrates careful consideration of production requirements: * Handles multiple question types (general, speaker-specific, detail-oriented) * Maintains context coherence through vector similarity checks * Implements engagement features through suggested questions * Provides video timestamp references for answers ## Technical Implementation Considerations The system shows careful attention to production-grade LLM implementation: * Error handling through confidence thresholds * Performance optimization through separate prompting stages * Quality control through cross-validation of results * Scalability through efficient vector database usage ## Challenges and Solutions Several key challenges were addressed in the implementation: * Long-form content processing: Solved through multi-level chunking * Speaker identification: Addressed through sophisticated prompt engineering * Answer verification: Handled through vector similarity checks * User engagement: Managed through suggested questions and timestamps ## Evaluation and Quality Control The system implements several quality control measures: * Multiple prompt validations for speaker identification * Context verification through vector similarity * Answer relevance checking against source material * User engagement monitoring through suggested questions ## Production Infrastructure The implementation required several infrastructure components: * Vector database for storing multiple representation levels * LLM integration for various processing tasks * Transcript processing pipeline * Real-time query processing system ## Future Considerations The case study mentions plans for future enhancements: * Integration of visual information processing * Expansion of speaker detection capabilities * Enhanced context understanding This implementation demonstrates a sophisticated understanding of LLMOps in production, showing how multiple AI components can work together to create a practical, user-facing system. The attention to accuracy, user experience, and system performance shows mature LLMOps practices, while the multi-level approach to content processing demonstrates innovative solutions to complex problems in video content understanding.