January 15, 2025
Technical Workshop
Beyond the Hype: Research-Proven Ways to Dodge LLM Project Failure
Evidence-based strategies from real-world failures and academic research
An evidence-based guide to avoiding common pitfalls in LLM projects, drawing from both academic research and real-world case studies. This presentation synthesizes findings from 50+ academic papers, industry reports from leading organizations (Gartner, McKinsey, MIT CISR, RAND), and real-world failure case studies to provide actionable strategies for successful AI implementation.
44 slides60 minAI Engineers, Technical Leaders, Product Managers, CTOs, Data Scientists, Researchers
AILLMGenAIMachine LearningMLOpsAI Failure PreventionResearch-BasedCase StudiesBest PracticesMulti-Agent SystemsPrompt EngineeringROIEnterprise AI
Key takeaways
- Start with business value: Define clear objectives and metrics before technology selection
- Invest in data quality: Data quality is the foundation of AI success
- Address hallucinations: Use RAG, human oversight, and validation systems