A Field Guide to Rapidly Improving AI Products -- With Hamel Husain

Hamel Hussain discusses the importance of evaluation as a comprehensive process in AI engineering, not just a metric, emphasizing the need to articulate and refine what "better" means for AI products through error analysis and data examination. He highlights the common oversight of fundamental data analysis skills in AI development, advocating for domain experts to easily modify prompts and for prioritizing error analysis to guide improvements, rather than preemptively creating numerous evaluations. Hussain also shares insights on balancing qualitative and quantitative evaluations, particularly in Retrieval-Augmented Generation (RAG) systems, and stresses that the iterative nature of AI development requires aligning metrics closely with user needs and continuously refining the vision of what the AI should achieve.