GenAI: mini simulator by gopractice
for Product Managers
Mini Simulator
Learn to apply generative AI to create products and automate processes through solving a practical business case.
- Learning time: 4–6 hours
- Format: simulator, self-paced
- Required knowledge: no programming skills or deep knowledge of mathematics required.
co-founder of Microsoft
"Generative AI has the potential to change the world in ways that we can’t even imagine"
Who is
the simulator for
"We made this mini simulator for those who see the enormous potential of generative AI but can't keep up with all the changes and are afraid of missing the window of opportunity"
Course author, 15 years of experience in AI/ML
Who can benefit from the simulator
- Product managers
- Entrepreneurs
- Analysts, marketers, and other specialists interested in the potential of AI
What you will get
- Understand the basics of generative AI
- Learn to uncover new possibilities for generative AI products
- Get familiar with the generative AI product development process
- Learn to work with unstructured data
- Understand the language of AI specialists
- Gain a competitive advantage for career growth
Program
No programming skills or deep knowledge of mathematics is required.
In the simulator, you will join an analytics team of a product with millions of users.
You will be responsible for automating user reviews analysis.
By solving this business problem, you will learn how to apply generative AI and unlock its huge potential.
- Basics of generative AI (GenAI)
- How large language models (LLMs) work
- What is prompt engineering
- Algorithm for designing prompts
Work on the project
- Introduction and study of the project
- What is an AI pipeline
- Algorithm for solving business problems using GenAI
Work on the project
- Prompt engineering for identifying topics and their sentiment in reviews
- Algorithm for evaluating prompt quality
- Datasets and metrics for quality evaluation
- The product manager's responsibility in GenAI projects
Work on the project
- Creating datasets, choosing metrics, and evaluating the quality of model responses
- Scaling AI solutions: from processing single reviews to large volumes
- Solving classification problems with GenAI
Work on the project
- Identifying main categories of topics in reviews and classifying topics by categories
- How to create value for users based on a working AI pipeline
- Visualizing data to extract business insights
Work on the project
- Analysis of trends in reviews and drawing conclusions based on them
- Components of AI systems
- How to build a product around AI system
- What are LLMOps and GenAIOps
- Deploying an implemented AI system
- Evaluating the business value of the solution
Work on the project
- Comparison with competitive solution
- Evaluating business value of the product
Authors
The creators of this simulator have over 15 years of experience in applied machine learning and artificial intelligence. In recent years, they have been working on GenAI projects in ecommerce and fintech.
- Head of the ML team at Joom (a marketplace with 400+ million users)
- Responsible for creating the machine learning infrastructure at Yandex
- Part of the early team of the Yandex Data Factory project
- Created ML solutions for large businesses in metallurgy, agriculture, and finance as head of the ML department
- Worked in the search quality evaluation team at Yandex
- Led the machine learning department at VeeRoute
- Founder of GoPractice — a leader in product management training
- Worked as a Data Scientist at Meta
- Led the analytics team at Yandex
Still have questions?
Email the GoPractice team at contacts@gopractice.io or via the messenger plug-in located at the bottom right, and we will respond as soon as possible.