An Introductory Guide to GenAI for Product Managers
FourthRev Team
Generative AI and large-language-models (LLMs) have become the workplace buzzwords of 2024. Microsoft reported that in the first half of 2024, the use of GenAI doubled. It’s easy to understand its quick adoption when you realise the productivity and efficiency pay-offs (among others):
Ninety per cent of artificial intelligence (AI) users say AI helps them save time, focus on key tasks, be more creative, and enables work to be more satisfying and rewarding.
The most diligent of AI users get back an entire day of work by using Copilot to summarise their meetings.
Within product engineering specifically, “AI can automate tasks, propose new design approaches, analyse material options, test prototypes, and make recommendations based on project parameters.”
The technology is also becoming synonymous with modern workplaces, so much so that 66% of leaders say they wouldn’t hire someone without GenAI skills.
To ensure you’re the one these leaders would hire, and to meet the growing demand for AI training, the King’s Product Management Career Accelerator now features a dedicated AI and Product Management Learning Track.
Become an AI-literate product manager
To give you a glimpse into the series that learners get full access to, you can watch the first Masterclass hosted by AI expert Alexander Cohen. You’ll learn about the foundations of AI and LLMs and the implications of AI on product management.
“Within five years, AI is going to make a fundamental change in the way that we produce products. One of those fundamental changes is that AI will in many respects replace human engineers. As such, we’ll get to a place where product managers are going to be responsible for defining what goes into a product and what it should do, and then an AI will execute on that.”
Alexander Cohen
Cheat sheet: An intro to GenAI
Following the Masterclass, Alex has created a guide to AI that you can refer to on the job and with your colleagues to help you understand what AI is, why it matters, and how to use it in your product management career.
Tokens
What it is
Tokens are the fragments of text that AI models use to analyse and generate language. For example, the sentence “Artificial intelligence is fascinating” might be broken down into tokens like [“Artificial”, “intelligence”, “is”, “fascinating”].
Why it matters
The way text is tokenised influences the model’s ability to understand and respond accurately. Proper tokenisation ensures the model captures the nuances of language, which is vital for applications like chatbots, translation services, and content creation.
For product managers, understanding tokens helps in designing AI applications that require precise language processing, ensuring that the outputs are relevant and coherent.
A context window is the span of text an AI can consider at once. For example, a model with a 4,000-token context window can process and generate responses based on that amount of text in a single go.
Why it matters
Larger context windows are crucial for maintaining coherence in long-form content. This means that for applications like document summarisation, extensive conversations, or complex problem-solving, the AI can keep track of more information, providing better and more accurate results.
For product managers, understanding the importance of context windows helps in selecting the right AI tools for tasks requiring detailed and sustained attention, such as user experience design, customer service automation, or content generation.
The implications of AI on product management
AI is playing a major role in the general labour market, and managers now say that AI aptitude could rival experience. For product managers, AI won’t take away your job but rather redefine what’s achievable in your work.
Through its automation and data analysis capabilities, AI impacts how certain tasks are performed and the speed at which they’re done. It’s also shifting the focus of product managers to tasks that require softer skills.
Use AI for market insights and opportunities
AI-driven analytics provide deep insights into market trends and customer behaviours, enabling data-informed decision-making. This helps in crafting precise value propositions and aligning product vision with market needs.
Predictive analytics and machine learning models assist in anticipating customer needs and identifying opportunities for innovation, enhancing strategic planning and competitive positioning.
Use AI for routine and repetitive tasks
AI automates routine tasks like scoping, documentation, and backlog management, improving efficiency and accuracy. Natural language processing (NLP) tools can generate detailed reports and documentation based on minimal input.
With AI handling repetitive tasks, product managers can focus on their softer skills, such as effective communication, stakeholder management, and team leadership, fostering better collaboration and aligning teams towards common goals.
“As AI starts to take over the heavy lifting of how products get built, we’re going to start to see this onus coming back to product managers on what actually goes into a product.”
Get hands-on with the concepts
Train an LLM
TensorFlow Playground is an interactive web-based tool designed to help users understand the basics of neural networks. It provides a visual and hands-on way to experiment with different neural network architectures and parameters.
Features
Interactive visualisation: Allows users to see how neural networks learn and adjust weights in real-time.
Adjustable parameters: Users can modify parameters such as learning rate, number of neurons, and activation functions to observe their impact on the model’s performance.
Educational tool: Ideal for beginners to get a grasp of fundamental concepts in machine learning and neural networks without requiring any programming knowledge.
Why it matters
Understanding how neural networks work at a basic level helps product managers and developers make informed decisions about integrating AI into their products. It demystifies the learning process of AI models and provides a foundation for more advanced AI concepts.
Ollama is a platform that allows users to run open-source AI models locally on their own hardware. This provides flexibility and control over AI processes without relying on external servers.
Features
Local model deployment: Users can deploy and run AI models directly on their machines, ensuring data privacy and security.
Support for various models: It’s compatible with a range of open-source models, enabling users to choose the best fit for their needs.
Customisability: Users can experiment with different model configurations and parameters to tailor the AI’s performance to specific tasks.
Why it matters
Running AI models locally with Ollama empowers product managers and developers to have full control over their AI applications. It enhances data privacy, reduces dependency on cloud services, and allows for more experimentation and customisation of AI models.
Hume.ai is a platform focused on creating empathic voice interfaces that can understand and respond to human emotions. It leverages advanced AI to analyse vocal expressions and deliver more natural and emotionally aware interactions.
Features
Emotion recognition: Uses AI to detect and interpret a wide range of human emotions from voice inputs.
Empathic responses: Generates responses that are emotionally appropriate, enhancing user engagement and satisfaction.
Voice interface customisation: Allows developers to tailor the voice interface to specific applications, ensuring that the emotional tone matches the intended user experience.
Why it matters
Understanding and responding to human emotions is crucial for creating engaging and effective AI-driven interactions. Hume.ai enables product managers to develop more intuitive and empathetic user interfaces, improving customer experiences in areas like customer service, virtual assistants, and interactive entertainment.
“We’re going to see that subject matter expertise, that domain knowledge, that real-world understanding of how things actually happen for real people, that’s going to be incredibly valuable and a real, great differentiator.
I’d be concerned about making sure you upscale yourself in those soft skills and really just driving a great outcome in terms of understanding that customer problem and understanding what those customers genuinely need and sorting through the noise and getting to the signal.”
Experience the full AI Learning Track
Learners of the King’s Product Management Career Accelerator get full access to the AI and Product Management Learning Track that features four live expert-led Masterclasses.
Designed to enhance the Career Accelerator’s curriculum and adapt as technology evolves, the Learning Track gives learners the latest tips and techniques to help them apply AI to their product management work.
Learners also earn a LinkedIn digital badge to showcase their AI literacy. To find out more about the Career Accelerator and Learning Track, download the programme brochure.
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