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.

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➡️ 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 Introduction
02:18 Excitement about Machine Learning and AI
03:28 Using LLMs as Backend Developers
04:22 Building Applications with LLMs
07:07 Building Instructor
09:30 Thinking in Logic and Design
10:33 Validating Data and Building Systems with Instructor
11:49 Thoughts About Product and UX in LLMs
17:51 Future of Instructor
20:25 Misconceptions and Unsolved Problems in LLMs
24:57 Improving LLM Applications
26:14 RAG as Recommendation Systems
29:32 Fine-tuning Embedding Models
32:32 Beyond Vector Similarity in RAG
39:32 Predictions for the Next Year in AI and ML
45:26 Measuring Impact on Business Outcomes
47:06 The Continuous Cycle of Machine Learning
48:38 Unlocking Economic Value through Structured Data Extraction
50:52 Questioning the Status Quo and Making an Impact