Vanishing Gradients Episode 21: Deploying LLMs in Production: Lessons Learned
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Nov 14, 2023 Guest Hamel Husain, a machine learning engineer, discusses the business value of large language models (LLMs) and generative AI. They cover common misconceptions, necessary skills, and techniques for working with LLMs. The podcast explores the challenges of working with ML software and chat GPT, the importance of data cleaning and analysis, and deploying LLMs in production with guardrails. They also discuss an AI-powered real estate CRM and optimizing marketing strategies through data analysis.
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Initial Skepticism about AI Code Generation
- Hamel initially doubted AI's ability to write code, especially after working on semantic search at GitHub.
- However, after witnessing the iterative improvements in models like CoPilot, he became a strong believer in their potential.
Using LLMs for Learning and Exploration
- Integrate LLMs into your workflow wherever possible to understand their capabilities and limitations.
- Use LLMs as learning tools by asking clarifying questions and exploring alternative approaches.
Iterative Prompt Engineering for Code Generation
- When using LLMs for coding, treat them as learning tools and engage actively with their output.
- Ask follow-up questions, explore alternatives, and verify the information provided to improve understanding and code quality.