Title: Building and Leading a Team for Generative AI Product
Abstract:
The advent of generative artificial intelligence (AI) has opened new possibilities forbusinesses to enhance customer interactions through conversational agents.
Building a successful generative AI product requires assembling a team with diverse
expertise and effective leadership to navigate the complex challenges associated withthis cutting-edge technology.
This lecture focuses on the process of building and leading a team for developing agenerative AI conversation bot specifically tailored for Booking.com, a leading online
travel platform. It explores the key considerations involved in assembling a proficientteam to deliver a high-quality conversational experience to customers.
By combining technical expertise and domain knowledge, the team created an AIsystem that understands user queries and provides accurate and relevant responses,
that maintain a consistent conversational style that aligns with Booking.com's brandvoice.
In the product, we considered aspects such as data privacy and security,
implementation of a moderation process, adherence to UX constraints, and an
integration of the bot with actual Booking.com recommendation cards. We have facedchallenges with prompt engineering and how to split the prompt instructions to best fit
our needs and flow. We constructed a new infrastructure that yielded several
noteworthy insights and lessons learned, which we would like to discuss and share.
The team incorporates data scientists, engineers, and user experience specialists toovercome these challenges, enabling the bot to deliver accurate information, assist inbookings, and provide exceptional and personalized recommendations while preserving
latency and scale of users.

Potential discussion points:-Expertise needed to develop GenAI based products. Simply forming someprompts and hoping for good results will not provide the needed outcome. The
only way to develop this expertise is by building GenAI based products hands-on.

-Prompt engineering tricks- Different prompts techniques need to be used toimprove the accuracy of LLMs - like breaking prompts to smaller tasks,
Chain-of-thought (CoT), providing examples in the prompt itself (Few-shot).

-Understanding the team roles - discuss the importance of teamwork indeveloping generative AI products. Explain how diverse skill sets andperspectives can contribute to a successful team.

-Leadership in Generative AI Projects - Discuss the unique challenges faced byleaders in the field of generative AI. Explore leadership styles and strategies thatare effective in motivating and guiding the team towards successful outcomes.

-How to deliver fast?- how to break the GenAI product to MVP, what should it
include?

-Future Trends and Opportunities- discuss emerging trends, research directions,and potential future opportunities of generative AI. How what we built can be still
relevant if new models are coming out.

An explanation about relevance of this talk/panel discussion to the workshop
The lecture on "Building and Leading a Team for Generative AI Product" is extremely
pertinent to the workshop as it equips the audience with valuable practical knowledge
and strategies for effectively constructing and guiding teams in the realm of generative
AI projects. Given the increasing significance and applicability of generative AI innumerous machine learning companies today, the insights shared during the lecture
hold immense value and relevance. By incorporating the knowledge imparted in the talk,workshop participants can directly apply it to their roles as managers and leaders ofapplied machine learning teams, empowering them to steer their teams towards
accomplishing successful and impactful generative AI projects.

Presenter information (LinkedIn)
Moran is a machine learning manager at booking.com, researching and developing
computer vision and NLP models for the tourism domain. Recently launched agenerative AI chatbot product designed for the travel domain.
Moran is a Ph.D candidate in information systems engineering at Ben Gurion University,
researching NLP aspects in temporal graphs.
Previously worked as a Data Science Team Leader at Diagnostic Robotics, building ML
solutions for the medical domain and NLP algorithms to extract clinical entities frommedical visit summaries.