Lessons from a Year of Building with LLMs

This podcast episode explores the challenges and opportunities in evaluating and developing large language models (LLMs). The speakers discuss the importance of guidance and tools in evaluating LLMs, the power of LLM evals in AI product development, the role of evals and data in AI engineering, the significance of data literacy, the potential of LLMs in data analysis, the importance of intelligent software, the collaboration among the speakers, the rise of data-centric AI application developers, the expectations for AI engineering roles, the factors that can derail a fine-tuning project, the concept of RAG systems, the process of fine-tuning models, the power of building synthetic worlds, the significance of trace analysis in LLMOps, the availability of trace viewer visualization, the benefits of Weave for experiment tracking, the importance of building end-to-end systems with LLMs, the gap between software engineers and data scientists in data literacy, the excitement for the future of robotics and AI, and the importance of iteration in building complex systems.