Ksenia Palke, Director of AI at Airspace, discusses the use of AI in time-critical shipments. Airspace builds tech for fast automated solutions in shipping critical items such as organs, medical devices, and industrial parts. The goal is to remove unnecessary manual steps and optimize the delivery process. Ksenia explains how AI is used to predict delivery times, optimize routes, and automate interactions with drivers and customers. She also discusses the use of large language models (LLMs) in their AI initiatives and the importance of prompt engineering and ragging. Ksenia emphasizes the need for context-specific training and the ongoing comparison of different LLMs. In this conversation, Etienne and Ksenia discuss the implementation of language models (LLMs) in business and the importance of being specific and intentional in their use. They emphasize the need for a clear strategy and goals when using LLMs, rather than chasing the latest and greatest technology. They also highlight the importance of having a human in the loop to validate and manage LLM outputs. The conversation explores the limitations of LLMs and the potential for future advancements. Overall, the conversation provides practical advice for founders and CTOs on how to approach AI projects and make the most of LLMs.

Time Stamps00:00 Introduction and Overview02:12 Automation and Optimization in Time-Critical Shipments09:22 Using LLMs to Automate Interactions14:05 The Importance of Prompt Engineering and Ragging in LLM Training24:14 Bottlenecks and Challenges in LLM Development29:29 Being Specific and Intentional with LLMs35:04 The Importance of Performance over Novelty39:18 The Role of Human Validation44:29 Avoiding the Hype and Focusing on Practical Applications53:26 Understanding the Limitations of LLMs57:47 Embracing Advancements in LLM Technology

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