So, you have an AI product, based on models you've trained with many resources and much thought (and perhaps some rule-based systems - don't be ashamed, everyone has those) and overall, you're pretty happy about it.

Then one day, comes the revolution of Large Language Models. The possibilities are endless! The sky opens up before you! What a time to be a data scientist!

But what will you do with the existing AI system, the invested and accurate one you've built up until now? Continue to develop it and ignore the LLMs? Discard it and start from scratch? Keep it as a backup for a rainy day?

In the last year, I've heard this dilemma repeatedly from data scientists and AI leaders. In Microsoft Healthcare and Life Sciences group we have experienced it as well, with our flagship Text Analytics for Health service.

In this lecture, I will resolve this dilemma by introducing the Hybrid AI approach and explaining how to build Hybrid AI systems that utilize both the strengths of LLMs and the advantages of your existing ML-based system. I will demonstrate three basic designs of such hybrid systems that we tried in our Data Science team, and shortly analyze their respective advantages and challenges.

At the end of the talk, you will no longer have to choose between LLMs and your current system - you will choose to enjoy the best of two worlds.