Building With AI Enabling startups to innovate with AI | Jason Liu (Independent Consultant)
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Jan 29, 2024 Jason Liu, a seasoned AI operator and consultant, discusses general applications vs. AI applications, how developers should work with LLMs, what LLMOps companies need to focus on, why saying 'no' can improve AI's success rate, building agile AI systems, best practices for RAG systems, and more!
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Treat AI as Normal Code
- Build AI applications with an emphasis on code quality, treating AI as a regular part of the codebase, like a database or server.
- Use tools like Pydantic or ZOD to enhance determinism and integrate AI seamlessly with existing code structures and practices.
Prioritize AI Skills over Specific Tools
- Dev tools for AI fall into two categories: specialized tools with limited use cases and versatile tools that teach valuable, transferable skills.
- Prioritize learning the core skills of working with AI, as those skills will be applicable even as specific tools become obsolete.
Business-Oriented AI Evaluation
- Evaluating AI systems requires understanding their limitations and focusing on business outcomes rather than generic metrics.
- While benchmarks like those used by OpenAI are useful for general capabilities, businesses need to tie evaluations to specific business goals.