When product guru Lenny Rachitsky shared this mini-course on Generative AI on LinkedIn, I was intrigued. It's typically priced at 299 USD but the course creator,, has made it available for free for a limited time. It promises to give the learner hands-on experience with GenAI in a product development context, which aligns with my personal goal of enhancing my skills for a career in AI Product Management. Having just completed the course, I wanted to share my honest thoughts on it to help anyone who is deliberating whether or not to take it.

Course Overview

The course spans six chapters and takes approximately 4 to 6 hours to complete. Over four sessions, I explored using tools like ChatGPT to automate the categorisation and analysis of large amounts of qualitative data—think thousands of user reviews. In the course creators' words, here’s what the course aims to impart:

  • 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

Here's the course program from

Chapter 1: Introduction. Generative AI Explained
Chapter 2: Review analysis: identifying topics and sentiments
Chapter 3: Prompt quality evaluation
Chapter 4: Identifying main categories of topics
Chapter 5: Visualizing and analyzing the results
Chapter 6: Developing a product around AI models

Who should Enrol:

The course is ideal for low-code and no-code PMs, entrepreneurs, UX researchers & designers, marketers, data analysts, and even academics who are interested in leveraging AI to enhance their workflows and processes. Also for anyone at a business who is currently paying a lot of money or spending a lot of time solving the scenario problem of continuous analysis and monitoring of qualitative feedback of their product.


It's not suitable for anyone who wants in-depth training on building a custom LLM from scratch. (see diagram below). This simulation does not let you practise: foundational model selection, deploying, or monitoring––though it does provide useful resources for learning about those topics. It does not cover fine-tuning or hyperparameter tuning at all. This simulator also does not teach how to use generative AI to visualise data.

Adapted from image by Vishnu Pamula in An Introduction to LLMOps: Operationalizing and Managing Large Language Models using Azure ML

Key Takeaways

The scenario offered was almost universally relatable for PMs, who frequently need to collate and make sense of the firehose of feedback about their products. Here's what I learned:

  • Data Quantification: The course demonstrated how GenAI can transform vast amounts of unstructured customer feedback into actionable quantitative data.
  • Cost-Benefit Analysis: I learned to assess when to employ LLMs for solving certain business challenges as opposed to alternative strategies.
  • Hands-on Model Testing: I gained practical experience in applying GenAI models to datasets to evaluate prompt quality.

On the topic of prompt engineering, I came to an interesting realisation that the instructors' lessons on prompt engineering, while still fairly useful on GPT-3.5, had very little effect on GPT-4. The LLMs are becoming so sophisticated that they're giving us useful answers even when we give them lazy or ill-designed prompts. This shift redirects the PM's attention from prompt optimisation to the more critical task of structuring the overarching system and defining the inputs and outputs of each step.

My Honest Review of the Course:

Course Quality:  🍵🍵🍵🍵 (out of 5 🍵)

Overall, I really enjoyed the course and it filled in some gaps in my knowledge of generative AI. The pages have a clean design and simple, addictive format, featuring a mix of text, images, and interactive multiple-choice questions to test your understanding, as well as links to high-quality external content for further learning. The reason I didn't give it full marks is that I expect something branded as a "simulator" to give me a richer, integrated experience, but this felt like more of a tutorial article that directs you to external tools such as Google Sheets, Google Docs, and ChatGPT, where most of the work takes place.

Value for Money: 🍵🍵🍵

For a free course, it's a no-brainer––about 80% of the content was quite enriching. But for the full price, I'd value it at more around the $99 to $199 mark. It's worth more than typical online courses (priced between $12 to $50 on platforms like Udemy and Coursera) due to its engaging user experience, the practical depth of the scenario provided, and of course, the topic of GenAI being super on-trend right now. But any more than $200 and I'd begin to feel like there's not enough content to justify the price, and you could take more comprehensive AI courses for $400 or $500 that even give you access to live instructors.

That said, this mini-course serves as an effective lead magnet for GoPractice, attracting users to their more expensive offerings, the Data-Driven Product Management Simulator (1,190 USD) and Product Growth Simulator (1,690 USD). If those premium courses offer more polished content than this one, then I would consider signing up.

Try it for free until 21 April

If this mini-course sounds at all interesting to you, I recommend taking advantage of the free access available until 21st April. Those who complete at least the first two chapters (“Generative AI Explained” and “Review analysis: identifying topics and sentiments”) by then will secure permanent free access. Sign up today at to expand your understanding of Generative AI in PM processes!

Disclaimer: I am not affiliated with or its creators.

Join the discussion below and let me know: Would you recommend this Generative AI course to your colleagues? Why or why not?

Get Free Access: Generative AI for PMs Mini Simulator on – Available only until 21 April 2024

Curious about the Generative AI for PMs Mini-Simulator from Check out my detailed review and see if it's right for you—especially while it’s still free! Ideal for no-code PMs, entrepreneurs, UX researchers & designers, marketers, data analysts, academics who want to leverage AI.