# Framework for Evaluating LLM Production Use Cases ## Background and Context This case study examines insights from Barak Turovsky, who brings extensive experience from both the first and second waves of AI implementation. His background includes: - Leading Google Translate during its transformation to neural networks - Being product manager during Google's TPU development - Current role as Executive in Residence at Scale Venture Partners ## The Two Waves of AI ### First Wave (2015-2016) - Marked by Google's breakthrough in using deep neural networks at scale - Required development of custom hardware (TPUs) with $130M investment - Initially limited to big tech companies (Google, Meta, Microsoft, Amazon) - Primary applications: assistants, speech, query understanding, ads ### Second Wave (Current) - Characterized by democratization of LLM capabilities - Broader industry applications beyond tech giants - More accessible to startups and smaller companies - Focus on practical implementation challenges ## Framework Components ### Key Dimensions - Accuracy Requirements: How precise the output needs to be - Fluency Needs: How natural and eloquent the language must be - Stakes: Consequences of mistakes or hallucinations ### Use Case Categories ### Green Zone (Recommended for Current Deployment) - Creative tasks (writing, poetry, music composition) - Workplace productivity (emails, presentations) - Characteristics: ### Yellow Zone (Proceed with Caution) - Business communications - Internal documentation - Characteristics: ### Red Zone (Not Yet Ready) - Search applications - High-stakes decision support - Financial transactions - Characteristics: ## Implementation Considerations ### Technical Challenges - Latency management - Cost optimization - Hallucination mitigation - Data preparation and cleanup - Integration with existing systems ### Organizational Requirements - New skill sets needed: - Process re-engineering - Tool adaptation ## Industry-Specific Applications ### Current Priority Areas - Entertainment and media - Customer service interactions - Software development - Education ### Implementation Strategies - Front-end vs. backend considerations - Integration with existing knowledge bases - Data retrieval and processing - Hybrid approaches combining LLMs with traditional systems ## Best Practices for Production ### Data Management - Focus on data engineering - Build robust knowledge bases - Implement proper vectorization - Maintain data quality and freshness ### System Architecture - Consider hybrid approaches - Plan for scalability - Implement proper monitoring - Build feedback loops ### Risk Management - Implement proper validation - Build in human oversight where needed - Consider privacy and security implications - Plan for exception handling ## Future Considerations ### Model Selection Strategy - Balance between proprietary and open-source models - Consider industry-specific customization - Evaluate cost-benefit tradeoffs - Plan for model updates and improvements ### Skill Development - Focus on LLM-specific capabilities - Maintain traditional ML/AI skills - Develop product understanding - Build cross-functional expertise ## Key Takeaways - Start with lower-risk, creative applications - Build trust through controlled deployment - Focus on use cases with built-in verification - Invest in proper infrastructure and skills - Plan for long-term evolution of capabilities ## Success Metrics - User adoption rates - Cost efficiency - Error rates and accuracy - Processing speed and latency - Customer satisfaction - Business impact and ROI