Conclusion #

Thanks to LLMS and GenAI, there is renewed interest in enterprise search. But if history offers any lesson, it is that relying on a magic box always disappoints. We have decades of research and findings on how to make enterprise search work, and we must use them to create user-centric search experiences that delight users. Yes, LLMs and GenAI not only help in fixing search but offer a whole new set of experiences.

Let me describe a proof of concept (POC) we did for a banking client five years ago when we had the old machine learning models. We built a search app to answer queries on credit card usage in SE Asia. Here's a use case of the app:

• A bank executive sees a QR code on a TV in his room.
• He scans the QR code with his mobile phone. The screen on the mobile phone reveals a Siri-like voice interface.
• Executive: "What is the credit card usage for this month in Myanmar?"
• The app shows a graph of credit card usage across the last six months.
• Executive: "Compare this to the numbers in Vietnam."
• The graph now responds to show two line charts.
• Executive: "Zoom in to the May-Jun period."
• The graph is updated to reflect the May-Jun numbers.
• Executive: "OK, send a picture of this to my email."
• The screen on the app says: Picture captured and sent to your email".

Yes, the POC could only understand a small set of commands, and it could not reply in voice, but you can imagine the kinds of experiences we can build with today's LLMs and GenAI capabilities.

We hope that the material in this book helps you to build search experiences that delight users. It is high time!