Panelists George Mathew, Asmitha Rathis, Natalia Burina, and Sahar Mor discuss building products with LLMs, emphasizing transparency, control, and explainability. They explore the challenges of prompting in language models and provide tips for avoiding impersonation and hallucination. They highlight the importance of feedback loops in improving language models and discuss the economic components of using APIs and inference calls. The panel concludes with excitement about the conference and promotion of their own podcast.
