In this episode, I had the pleasure of speaking with Jason Liu, an applied AI consultant and the creator of Instructor – an open-source tool for extracting structured data from LLM outputs. We chat about LLM applications, their challenges, and how to overcome them. We also dive into Instructor, making LLMs interact with existing systems and a bunch of other cool things. Join our Discord community: https://discord.gg/tEYvqxwhah ➡️ Jason Liu on Twitter – https://twitter.com/jxnlco🤖 Instructor Blog – https://jxnl.github.io/instructor/🌐 Check Out Our Website! https://dagshub.com Social Links: ➡️ LinkedIn: https://www.linkedin.com/company/dagshub ➡️ Twitter: https://twitter.com/TheRealDAGsHub ➡️ Dean Pleban: https://twitter.com/DeanPlbn

Timestamps:00:00 Introduction02:18 Excitement about Machine Learning and AI03:28 Using LLMs as Backend Developers04:22 Building Applications with LLMs07:07 Building Instructor09:30 Thinking in Logic and Design10:33 Validating Data and Building Systems with Instructor11:49 Thoughts About Product and UX in LLMs17:51 Future of Instructor20:25 Misconceptions and Unsolved Problems in LLMs24:57 Improving LLM Applications26:14 RAG as Recommendation Systems29:32 Fine-tuning Embedding Models32:32 Beyond Vector Similarity in RAG39:32 Predictions for the Next Year in AI and ML45:26 Measuring Impact on Business Outcomes47:06 The Continuous Cycle of Machine Learning48:38 Unlocking Economic Value through Structured Data Extraction50:52 Questioning the Status Quo and Making an Impact