The Data Exchange with Ben Lorica Beyond the Demo: Building AI Systems That Actually Work
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May 1, 2025 Hamel Husain, founder of Parlance Labs and author of AI Essentials for Tech Executives, dives into the essential data science skills often missing from AI education. He underlines the need for collaboration between engineers and domain experts to tackle obstacles in AI development. The conversation explores practical strategies like generating synthetic data for better testing and touches on the evolving landscape of education in an AI-driven world, questioning the necessity of traditional college paths in favor of hands-on experience.
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Prioritize Data and Error Analysis
- Always look at your data carefully when building AI systems.
- Use error analysis to categorize and prioritize failure modes for targeted improvements.
Focus on AI Processes Not Tools
- Focus on developing effective AI processes, not just buying tools.
- Understand that measuring and evaluating your AI system requires iterative, careful effort.
Use Synthetic Data for Testing
- Generate synthetic user data with LLMs to test AI systems before having real users.
- Brainstorm different personas and scenarios to uncover potential failure modes early.