About this Video

• Video Title: Make your LLM app a Domain Expert: How to Build an Expert System
• Channel: AI Engineer
• Speakers: Christopher Lovejoy
• Duration: 00:19:18

Introduction

This video explains how to build a domain-native LLM application, focusing on the importance of the system's design over model sophistication. Christopher Lovejoy, a medical doctor turned AI engineer, uses his experience at Anterior, a healthcare AI company, to illustrate the "last mile problem" and how a robust system incorporating domain expertise can significantly boost performance.

Key Takeaways

• The Last Mile Problem: Successfully applying LLMs to specialized industries is hindered by the "last mile problem"—providing the model with sufficient context and understanding of the specific workflow.
• System over Model: The system designed for incorporating domain insights is more crucial than model sophistication for achieving high accuracy in vertical AI applications. Improving the system can lead to dramatic performance gains (e.g., from 95% to 99% accuracy).
• Adaptive Domain Intelligence Engine: This system comprises measurement (defining user-centric metrics and failure modes) and improvement (using production data to iteratively refine the pipeline based on identified weaknesses).
• Domain Expert Dashboard: A tool enabling domain experts to review AI outputs, label failure modes, and suggest improvements, creating a rapid iteration loop for performance enhancement.
• Iterative Improvement: The system facilitates a continuous feedback loop where domain experts identify weaknesses, engineers address them, and the impact is measured, resulting in a self-improving, data-driven process.