Generative AI for Product Management: Gen AI for PMs

What Is Generative AI: Our Approach and Use Cases

Generative AI goes beyond traditional AI’s ability to predict or classify — it can reason, synthesize information, generate new content, and create structured outputs based on a prompt. This lecture introduces the foundations of Generative AI and explains how this shift changes what product managers can accomplish across the product development process.

You’ll explore the difference between traditional AI and Generative AI, understand the course’s practical “black box” approach, and examine three ways product professionals can use Generative AI: general-purpose AI tools, specialized product management tools, and custom AI-powered solutions. The lecture also establishes an important principle for working with AI: understanding its capabilities and limitations while keeping human judgment and source verification at the center.

In This Lecture, You Will:

▸ Understand what Generative AI is and how it creates new content, ideas, and solutions from learned patterns

▸ Differentiate between traditional AI, which commonly focuses on prediction and classification, and Generative AI, which can support reasoning, synthesis, and creation

▸ Explore how Generative AI can work across text, images, audio, video, code, and applications

▸ Examine how Generative AI can support product work by reading information, extracting insights, identifying patterns, and creating structured deliverables

▸ Understand how Generative AI can move product teams beyond simply classifying information toward synthesizing research, identifying patterns, and creating problem statements

▸ Recognize the course’s practical “black box” approach, focusing on how product managers can use AI effectively rather than learning the underlying mathematics, architecture, or model training

▸ Understand the three things product professionals need to consider when using AI: what to provide, what to expect from the output, and where the tool’s limitations are

▸ Recognize that Generative AI can produce outputs that are wrong, incomplete, or confidently misleading, making source verification and human judgment essential

▸ Explore three practical approaches to using Generative AI: general-purpose AI tools, specialized product management tools, and custom AI-powered solutions

▸ Examine how general-purpose tools such as ChatGPT, Claude, Gemini, and Perplexity can provide flexible support across different product tasks

▸ Understand the practical considerations of using general-purpose AI tools, including ease of access, technical setup, privacy, organizational policies, and output quality

▸ Explore how specialized AI tools can support product activities such as user research synthesis, persona generation, roadmap prioritization, and user story writing

▸ Understand how organizations can create custom AI-powered solutions around internal workflows, organizational data, security requirements, and product processes

▸ Recognize the trade-offs between different approaches, including flexibility, specialization, privacy, cost, technical resources, and maintenance requirements

Who This Lecture Is For:

• Product Managers and Product Leaders looking to understand how Generative AI can support product work

• Product Designers and UX Researchers exploring AI-assisted research, synthesis, and creation

• Innovation and Strategy Professionals evaluating practical applications of Generative AI

• Business and Technology Professionals involved in adopting AI within product workflows

• Professionals New to Generative AI who want a practical understanding without needing deep technical knowledge

Why This Lecture Is Useful for Product Professionals

Understanding what Generative AI can actually do gives product professionals a foundation for identifying where it can add value across their work. By learning the difference between traditional and Generative AI, understanding its limitations, and comparing different approaches to adoption, product professionals can approach AI with practical expectations, informed judgment, and a clearer understanding of where it fits into product workflows.

Generative AI in Action: From Prompts to Business Deliverables

Generative AI has moved far beyond simply asking for ideas or generating text. In this lecture, you’ll see what that shift looks like in practice through a real product management scenario, where a single detailed request can drive research, analysis, synthesis, and the creation of usable business deliverables.

You’ll follow a practical demonstration of ChatGPT Agent Mode as it researches fintech onboarding experiences, analyzes public sources and user reviews, identifies UX patterns and friction points, and turns the findings into a competitive analysis PowerPoint and five evidence-backed problem statements. The lecture also highlights why source verification and human review remain essential, even as AI takes on more of the research and analysis workload.

In This Lecture, You Will:

▸ Understand how Generative AI has evolved from simple idea generation to handling multi-step product management tasks

▸ Explore how a single detailed prompt can brief an AI system in a way similar to briefing a human product analyst

▸ See how AI can combine reasoning, web research, and computer interaction to complete a complex product research task

▸ Examine how AI can research competitor onboarding flows, activation steps, friction points, and UX patterns

▸ Explore how AI can gather publicly available user feedback from app store reviews, forums, and product review sites

▸ Understand how research findings can be synthesized into five structured product problem statements

▸ See how AI can transform research findings into usable business deliverables, including a competitive analysis presentation and structured document

▸ Understand the importance of grounding AI-generated research in current external sources rather than relying only on information already contained in the model

▸ Recognize why product managers should review and verify the sources and claims behind AI-generated research

▸ Explore how Generative AI can reduce the manual effort involved in researching, synthesizing, and preparing product deliverables

▸ Recognize that AI can take on significant research and analysis work while human review and validation remain essential

Tools & Features Covered:

• ChatGPT Agent Mode — demonstrated for completing a multi-step product research and analysis task

• Deep Search — demonstrated for researching and reading information from multiple external sources

• Operator Functionality — demonstrated through AI interaction with websites and a virtual computer environment

• PowerPoint and Structured Document Generation — demonstrated as downloadable outputs from the completed research task

Who This Lecture Is For:

• Product Managers and Product Leaders who want to understand how Generative AI can handle complex product research tasks

• Product Designers and UX Researchers interested in using AI to analyze competitors, user feedback, and UX patterns

• Innovation and Strategy Professionals exploring practical applications of agentic AI in product workflows

• Business and Technology Professionals involved in evaluating AI-powered workflows and business deliverables

• Professionals Learning Generative AI for Product Work who want to see how AI capabilities translate into practical product outcomes

Why This Lecture Is Useful for Product Professionals

This lecture makes the capabilities of modern Generative AI concrete by showing how a product research task can move from one detailed prompt to researched insights and usable business deliverables. It helps product professionals understand the growing role of AI in research and synthesis while reinforcing the importance of source verification, review, and human judgment before using AI-generated outputs in real product decisions.

Building Working Product Tools with Generative AI

Generative AI can now move beyond generating text and insights to building working, interactive applications from natural language instructions. In this lecture, you’ll see how this capability can reduce the gap between a product idea and something tangible that can be tested, refined, and shared.

Through a practical demonstration in Google AI Studio, you’ll see a product manager create a Feature Gap Analyzer that combines a user problem statement, feature description, and user research documents. You’ll also see how the application can be refined through follow-up prompts, expanded to analyze multiple PDF files, and corrected when errors occur — all without directly writing code.

In This Lecture, You Will:

▸ Understand how Generative AI can act as a builder, creating working tools and applications rather than only generating text or research insights

▸ Explore how natural language instructions can be used to move from a product problem or idea to a working, testable application

▸ Examine how AI-powered building can reduce the traditional gap between product management, design, and development

▸ See how a product manager can create a tool for testing whether a proposed feature addresses an identified user problem

▸ Understand how AI can help product managers move from creating individual artifacts to becoming strategic editors and decision makers

▸ Explore how working prototypes can be created and refined without requiring engineering support for every small change

▸ Understand how Generative AI can reduce mechanical work and give product managers more time for strategic thinking and decision-making

▸ Recognize that faster generation and automation do not automatically produce better product decisions

▸ Understand why AI-generated outputs should be treated as a starting point rather than a finished answer

▸ Explore the principle of AI augmentation, where AI supports product professionals while human expertise remains responsible for judgment, context, and strategic decisions

▸ Recognize why product managers still need to consider factors such as company positioning, internal constraints, user needs, and strategic trade-offs

Tools & Features Covered:

• Google AI Studio — Build: Demonstrated for creating a working application from natural language instructions

• Natural-Language App Building: Demonstrated through the creation of the Feature Gap Analyzer

• PDF File Uploads: Demonstrated for adding user research documents and interview transcripts to the application

• Multiple PDF Uploads and Combined Analysis: Demonstrated as an enhancement to the application

• Iterative App Refinement: Demonstrated through follow-up prompts that update the application’s functionality and interface

• AI-Assisted Error Fixing: Demonstrated through Gemini identifying an application error and offering to resolve it

Who This Lecture Is For:

• Product Managers and Product Leaders who want to build and test product ideas more quickly with Generative AI

• Product Designers and UX Researchers interested in creating lightweight tools for research and product validation

• Innovation and Strategy Professionals exploring AI-powered prototyping and experimentation

• Business and Technology Professionals interested in understanding how AI can support product workflows

• Professionals New to AI-Powered Building who want to explore creating working applications through natural language

Why This Lecture Is Useful for Product Professionals

This lecture shows how Generative AI can shorten the distance between a product idea, a working prototype, and an opportunity to test it. By seeing how a product manager can build and refine a functional tool through natural language, you’ll understand how AI can reduce mechanical friction while keeping strategic judgment with the product professional. The lecture also reinforces that speed and automation must be balanced with critical thinking, product expertise, and human judgment.

What Is a Prompt? Understanding Context and Better AI Instructions

A good AI output often depends on how clearly you communicate what you want. This lecture introduces the foundation of prompt engineering by showing how the same AI tool can produce very different results depending on the instructions and context provided.

Through a live demonstration in Google Gemini, you’ll compare a simple, zero-context request with a detailed prompt that provides relevant context, requirements, and output formatting. The lecture establishes why clear instructions and sufficient context matter when working with AI.

In This Lecture, You Will:

▸ Understand what a prompt is and why it determines what you ask an AI tool to do

▸ Explore the difference between a vague instruction and a prompt that provides complete context

▸ Understand the concept of a zero-context prompt and how it can lead to basic or less relevant outputs

▸ See how providing details such as personal context, requirements, constraints, and desired format can improve an AI response

▸ Compare the output produced by a simple request with the output produced by a detailed, contextual prompt

▸ Understand how the way you communicate a request can significantly change the usefulness and relevance of an AI-generated response

▸ Recognize that prompt engineering concepts can be applied across different AI tools, even though the demonstrations in this course use Google Gemini and Google AI Studio

Tools & Features Covered:

• Google Gemini — demonstrated for comparing a zero-context prompt with a detailed contextual prompt,

  demonstrated through the structured diet-plan example

Who This Lecture Is For:

• Product Managers and Product Professionals who use AI for everyday product work

• Students and Early-Career Professionals learning how to communicate effectively with AI tools

• Creators and Business Professionals who want more useful and relevant AI-generated outputs

• Professionals New to Prompt Engineering who want to understand the fundamentals of effective prompting

Why This Lecture Is Useful for Product Professionals

Understanding how prompts work helps product professionals communicate their requirements more clearly to AI and get outputs that are more relevant, structured, and aligned with the task. This lecture provides the foundation for using context and clear instructions effectively before moving into more structured prompting frameworks.

The CREATE Framework: Six Pillars of Effective Prompting

A strong prompt requires more than simply telling AI what you want. In this lecture, you’ll learn the CREATE framework, a six-part structure designed to help you provide the right context, instructions, tone, and expectations in a single prompt. You’ll see how structuring these elements together can produce a more precise result without repeatedly revising your request.

Through a live demonstration in Google Gemini, you’ll compare a basic work-from-home policy email request with a prompt built using all six CREATE elements. The lecture shows how Character, Result, Examples, Actionable Instructions, Tone, and Expectations can guide AI toward a more specific and usable output in one attempt.

In This Lecture, You Will:

▸ Understand the six pillars of the CREATE framework for structuring effective AI prompts

▸ Learn how Character tells AI what role or expertise it should adopt

▸ Understand how Result clearly defines what you want AI to produce

▸ Explore how providing Examples can help AI understand the direction and desired result

▸ Learn how Actionable Instructions define the specific rules AI should follow

▸ Understand how Tone guides the voice and style of the AI-generated response

▸ Explore how Expectations communicate what AI should avoid, such as adding fictional information

▸ Recognize how including all six elements in one prompt can reduce the need for repeated prompting and iteration

▸ Compare a basic AI request with a prompt structured using the complete CREATE framework

▸ See how the CREATE framework can be applied to a practical work-from-home policy announcement email

Tools & Features Covered:

• Google Gemini — demonstrated for comparing basic prompting with the CREATE framework

Who This Lecture Is For:

• Product Managers and Product Professionals who use AI to create and refine work outputs

• Students and Early-Career Professionals learning structured prompting techniques

• Creators and Business Professionals who want more controlled and consistent AI outputs

• Professionals New to Prompt Engineering who want a practical framework for structuring prompts

Why This Lecture Is Useful for Product Professionals

The CREATE framework gives product professionals a practical way to structure their instructions before sending them to AI. By clearly defining the role, desired result, examples, instructions, tone, and expectations, product professionals can communicate requirements more precisely and reduce unnecessary prompt iteration.

Zero-Shot and Few-Shot Prompting: Guiding AI with Examples

Sometimes, explaining exactly what you want in words is difficult—especially when the required output follows an unusual or highly specific format. In this lecture, you’ll learn how Zero-Shot and Few-Shot prompting provide two different ways to guide AI, including how examples can communicate a desired pattern when written instructions alone are not enough.

Through a live demonstration in Google Gemini, you’ll compare a Zero-Shot prompt with a Few-Shot prompt for extracting information from a doctor’s note. You’ll see how providing examples can help AI recognize an unusual output pattern and reproduce it accurately.

In This Lecture, You Will:

▸ Understand what Zero-Shot prompting means and how it works without providing examples

▸ Understand what Few-Shot prompting means and how examples can guide AI toward a desired pattern

▸ Compare Zero-Shot and Few-Shot prompting and understand when examples can be useful

▸ Explore how written instructions may not always be enough to communicate an unusual or highly specific output format

▸ Learn how examples can demonstrate the desired pattern without requiring you to describe every formatting rule in words

▸ See how Few-Shot prompting can help AI reproduce a specific output structure based on the examples provided

▸ Compare the results of asking AI to follow an unusual format directly versus showing the format through examples

▸ Understand how providing multiple examples can help AI identify the intended pattern and produce the requested output

Tools & Features Covered:

• Google Gemini — demonstrated for comparing Zero-Shot and Few-Shot prompting

Who This Lecture Is For:

• Product Managers and Product Professionals who use AI for structured information and content tasks

• Students and Early-Career Professionals learning practical prompt engineering techniques

• Creators and Business Professionals who need AI to follow specific output patterns

• Professionals New to Prompt Engineering who want to understand how examples can guide AI responses

Why This Lecture Is Useful for Product Professionals

Product work often requires AI outputs to follow specific structures, formats, and patterns. Understanding Zero-Shot and Few-Shot prompting helps product professionals recognize when direct instructions may be insufficient and when examples can communicate the desired result more effectively.

Thinking Models: How AI Reasons Through Complex Problems

AI models have evolved from producing immediate responses to models designed to reason through complex problems before giving an answer. In this lecture, you’ll explore the difference between non-reasoning and reasoning models and understand why model selection matters when a task requires logic, mathematics, or coding.

Through a live test in Google AI Studio, you’ll compare two models using the same complex logical puzzle and the same strict condition. You’ll see how a non-reasoning model responds immediately, while a thinking model works through the problem step by step before producing its answer.

In This Lecture, You Will:

▸ Understand the difference between non-reasoning models and reasoning or thinking models

▸ Explore how thinking models approach complex problems before producing an answer

▸ Compare how reasoning and non-reasoning models respond to the same logical problem

▸ Observe how a non-reasoning model produces an immediate response without a visible thinking phase

▸ Examine how a thinking model works through a complex logical puzzle before providing its answer

▸ See how model selection can affect the outcome of tasks involving logic, coding, and mathematics

▸ Understand why choosing an appropriate reasoning model can be important for tasks that require deeper problem-solving

Tools & Features Covered:

• Google AI Studio — used for the live comparison of reasoning and non-reasoning models

• Gemini 3.1 Flashlight — demonstrated as the non-reasoning model

• Gemini 3.1 Pro — demonstrated as the reasoning model

Who This Lecture Is For:

• Product Managers and Product Professionals who need to understand how different AI models handle complex tasks

• AI and Technology Professionals exploring reasoning capabilities in generative AI

• Students and Early-Career Professionals learning how to choose models for different types of AI tasks

• Professionals Working with AI Tools who want to understand the practical difference between reasoning and non-reasoning models

Why This Lecture Is Useful for Product Professionals

Product professionals increasingly work with AI for tasks that require logic, mathematics, coding, and structured problem-solving. Understanding the difference between reasoning and non-reasoning models helps you recognize why the choice of model can affect how AI approaches complex tasks.

Role Prompting: Shaping AI’s Perspective and Output

Role prompting is a technique where you give AI a specific character or professional perspective before asking it to complete a task. In this lecture, you’ll explore why role prompting remains useful with newer thinking models—not as a way to trick AI, but as a way to establish the perspective, tone, and scope of its response.

Through a live demonstration in Google Gemini, you’ll see how the same business question produces very different responses when AI is given contrasting roles. The demonstration shows how the character assigned in a prompt can influence the tone, perspective, and type of ideas included in the output.

In This Lecture, You Will:

▸ Understand what role prompting is and how it gives AI a specific character or perspective

▸ Explore why role prompting can still be useful when working with newer thinking models

▸ Understand how assigning a role can establish the perspective and scope for an AI task

▸ Examine how different roles can influence the tone and direction of AI’s response

▸ Compare responses to the same business question when AI is given a negative role versus a professional role

▸ Observe how a lazy and frustrated employee role produces a different style of response from a business consultant role

▸ See how a professional role can lead AI toward business concepts such as value-based pricing, product bundling, upselling, and customer lifetime value

▸ Recognize how the character assigned to AI can influence the type and scope of the output

Tools & Features Covered:

• Google Gemini — used to demonstrate how different role prompts affect AI responses

Who This Lecture Is For:

• Product Managers and Product Professionals who use AI for business strategy and problem-solving

• Business Professionals and Managers who want more relevant AI responses for business tasks

• AI and Prompt Engineering Learners developing practical prompting skills

• Students and Early-Career Professionals learning how to guide AI outputs through role-based prompts

Why This Lecture Is Useful for Product Professionals

Product professionals often use AI for tasks that require a particular business perspective, tone, or area of expertise. Understanding role prompting helps you see how the role assigned in a prompt can shape the scope and direction of the resulting output.

The SCENE Framework: Creating Detailed Images with AI

Modern AI can work beyond text—it can understand images and videos and create visuals from words. In this lecture, you’ll learn why creating effective visual prompts requires thinking more like a director than a writer, and how the SCENE framework can help you build detailed image prompts step by step.

Through a live demonstration in Google Gemini, you’ll start with a simple prompt for a futuristic flying car and progressively add the elements of the SCENE framework. You’ll see how changing the camera angle, environment, lighting, and art style transforms a basic image into a more detailed and specific visual.

In This Lecture, You Will:

▸ Understand what Multimodal AI means and how AI can work with images and videos alongside text

▸ Explore why visual prompting requires a different approach from writing prompts for text-based outputs

▸ Learn the SCENE framework for creating detailed image prompts

▸ Understand S — Subject, which defines who or what appears in the image

▸ Understand C — Camera, which defines the shot and perspective, such as a close-up or wide-angle view

▸ Understand E — Environment, which describes what is happening in the background and surrounding setting

▸ Understand N — Nuances and Lighting, including the lighting conditions and visual atmosphere

▸ Understand E — Extra Style, which defines the visual or artistic style of the image

▸ See how progressively adding SCENE elements can transform a simple image into a more detailed visual

▸ Observe how camera angle, environment, lighting, and art style affect the final AI-generated image

Tools & Features Covered:

• Google Gemini — used to demonstrate the SCENE framework for image prompting

Who This Lecture Is For:

• Product Managers and Product Professionals who use AI to communicate product concepts visually

• Designers and Creative Professionals who want to create more detailed visuals with AI

• Marketing and Content Professionals creating AI-generated visual content

• AI and Prompt Engineering Learners developing practical multimodal prompting skills

• Students and Early-Career Professionals exploring AI image generation

Why This Lecture Is Useful for Product Professionals

Product professionals can use AI-generated visuals to communicate ideas, concepts, and scenarios more clearly. The SCENE framework provides a structured way to describe the subject, camera, environment, lighting, and style needed to create more specific visual outputs.

System Prompts and Temperature: Controlling AI’s Behavior

In this lecture, you’ll move to the developer side of prompt engineering and explore how backend controls can shape AI’s behavior across a conversation.

Through a live demonstration in Google AI Studio, you’ll learn how System Instructions can establish persistent rules for a chat session and how the Temperature setting controls the level of randomness and creativity in AI responses. You’ll see how these controls can be adjusted for different types of tasks, from analytical work to creative content.

In This Lecture, You Will:

▸ Understand the difference between a regular prompt and a system prompt

▸ Explore how System Instructions can establish rules that remain active throughout a chat session

▸ See how a system instruction can influence AI’s response to subsequent questions

▸ Understand how the Temperature setting affects the randomness and creativity of AI responses

▸ Compare lower and higher temperature settings and how they can change the style of AI output

▸ Explore why lower temperature settings can be useful for tasks where focus and accuracy are important

▸ Understand why higher temperature settings can be useful for creative writing, stories, and marketing ideas

▸ Learn how system instructions and temperature provide additional controls for shaping AI behavior

Tools & Features Covered:

• Google AI Studio — used to demonstrate backend prompt controls

• System Instructions — demonstrated for establishing persistent rules during a chat session

• Temperature — demonstrated for controlling randomness and creativity in AI responses

Who This Lecture Is For:

• Product Managers and Product Professionals who want greater control over AI behavior and outputs

• AI and Technology Professionals exploring practical AI configuration controls

• Prompt Engineering Learners who want to move beyond basic chat prompts

• Developers and Technical Professionals working with AI model settings

• Students and Early-Career Professionals building practical knowledge of AI controls

Why This Lecture Is Useful for Product Professionals

Understanding System Instructions and Temperature helps product professionals see how AI behavior can be configured beyond the visible chat prompt. These controls provide practical ways to establish consistent rules and adjust the balance between focused responses and creative outputs.

Giving AI External Tools: Code Execution and Google Search

Large Language Models are powerful at working with language, but they can struggle with tasks such as complex calculations and information that requires up-to-date knowledge. In this lecture, you’ll learn how external tools can extend AI’s capabilities by allowing it to execute code and access current information.

Through live demonstrations in Google AI Studio, you’ll explore Code Execution using the Program of Thought technique to solve a mathematical problem with Python, and Google Search through grounding to retrieve current information from the web. You’ll see how these tools allow AI to rely on external computation and current data rather than handling everything through language generation alone.

In This Lecture, You Will:

▸ Understand why Large Language Models can struggle with complex mathematical calculations

▸ Explore how external tools can extend the capabilities of AI beyond language generation

▸ Learn how Program of Thought uses code to help AI handle mathematical calculations

▸ See how AI can write and execute Python code to calculate the total of prime numbers between 1 and 500

▸ Understand how Code Execution allows AI to use a real computing environment to perform calculations

▸ Explore the knowledge cut-off limitation of AI when working with information that changes over time

▸ Understand Grounding and how it connects AI responses to current information from the internet

▸ See how Google Search can be used to retrieve and summarize current stock market news

▸ Compare how AI can use external computation and current web information to address different limitations

Tools & Features Covered:

• Google AI Studio — used to demonstrate external AI tools

• Code Execution — demonstrated for running Python code to solve a mathematical calculation

• Google Search / Grounding — demonstrated for retrieving current information from the internet

Who This Lecture Is For:

• Product Managers and Product Professionals who use AI for research, analysis, and data-driven tasks

• AI and Technology Professionals exploring ways to extend AI capabilities with external tools

• Data and Analytics Professionals interested in AI-assisted computation

• Prompt Engineering Learners who want to understand tool-enabled prompting techniques

• Students and Early-Career Professionals learning practical applications of AI tools

Why This Lecture Is Useful for Product Professionals

Product professionals often work with calculations, research, and time-sensitive information. Understanding Code Execution and Grounding helps you recognize how external tools can provide AI with computational capabilities and access to more current information when a task requires them.

Using AI Responsibly: Hallucination, Data Privacy, and Prompt Practice

Prompt engineering can help you get more useful results from AI, but using AI effectively also requires understanding its limitations and risks. In this final lecture, you’ll learn about two important concerns—hallucination and data privacy—and explore practical habits for using AI more safely and responsibly.

You’ll also learn why prompt engineering develops through practice and experimentation, rather than memorization. The lecture concludes with a practical approach for building your own prompt library by saving prompts that consistently produce useful results.

In This Lecture, You Will:

▸ Understand what AI hallucination means and how AI can confidently produce false information

▸ Recognize why AI-generated information should not be blindly trusted, particularly for financial, medical, and legal matters

▸ Learn the importance of cross-checking important AI-generated information using reliable sources

▸ Explore the risks of sharing sensitive information with open AI tools

▸ Identify information that should not be pasted into AI tools, including company secrets, bank details, sensitive client data, and passwords

▸ Understand the importance of checking data training settings when using AI tools

▸ See how data training settings can be accessed through ChatGPT Settings → Data Controls

▸ Recognize why prompt engineering develops through practice and experimentation rather than memorization

▸ Learn how to create a personal prompt library by saving prompts that produce useful results

▸ Understand the importance of maintaining human judgment while using AI as an assistant

Tools & Features Covered:

• ChatGPT — demonstrated for accessing data training settings

• Data Controls — demonstrated for managing the data training option in ChatGPT

Who This Lecture Is For:

• Product Managers and Product Professionals who use AI for research, analysis, and everyday product work

• Business Professionals and Managers working with AI-generated information and sensitive business data

• AI and Prompt Engineering Learners who want to understand responsible AI usage

• Professionals Handling Sensitive Information who need to consider privacy when using AI tools

• Students and Early-Career Professionals building practical and responsible AI habits

Why This Lecture Is Useful for AI Users and Product Professionals

Using AI effectively involves more than generating good prompts. Understanding hallucination, data privacy, verification, and safe data practices helps professionals use AI more responsibly. Building a personal prompt library also provides a practical way to turn repeated experimentation into reusable prompting knowledge.

AI-Powered Problem Discovery: Using Claude and NotebookLM

You can build a beautifully designed product, launch it successfully, and still fail to get adoption if you started with the wrong problem. That is why problem discovery is one of the most important stages of product design—and one where product teams often spend significant time working through interviews, reviews, support tickets, surveys, and other user feedback.

Generative AI can make this process much more manageable by helping product teams identify patterns across large volumes of unstructured research data. In this lecture, you’ll explore two tools used for different discovery scenarios: Claude for synthesizing a large body of text in a sustained conversation, and NotebookLM for bringing multiple research sources into one workspace.

In This Lecture, You Will:

▸ Understand why problem discovery is a critical first stage of the product design process

▸ Explore how getting problem discovery wrong can lead to building solutions for problems users do not actually have

▸ Understand the traditional process of analyzing user interviews, support tickets, app store reviews, churn reasons, and other research sources

▸ Recognize the challenges of manually synthesizing large volumes of unstructured user research

▸ Explore how generative AI can help identify patterns across large amounts of unstructured text

▸ Understand how AI can accelerate the mechanical part of research synthesis while human judgment remains important for interpretation, prioritization, and strategy

▸ Explore how Claude can support synthesis when research is concentrated in one large body of text

▸ Understand how NotebookLM supports research scenarios where information is distributed across multiple documents

▸ Compare the different research scenarios suited to Claude and NotebookLM

▸ Understand how NotebookLM brings multiple sources into a single research workspace and connects insights back to their source documents

Tools & Features Covered:

• Claude — introduced for synthesizing a large body of research text in a sustained conversation

• NotebookLM — introduced for working across multiple research documents in a single research workspace

• Source Citations in NotebookLM — explained as a way to trace insights back to their specific source documents

Who This Lecture Is For:

• Product Managers and Product Professionals involved in user research and problem discovery

• UX Researchers and Product Researchers working with interviews, reviews, surveys, and support data

• Innovation and Strategy Professionals analyzing user feedback to identify important problems

• Product Designers involved in understanding user needs before developing solutions

• Early-Career Product Professionals learning how generative AI can support research and discovery

Why This Lecture Is Useful for Product Professionals

Problem discovery often involves working across large amounts of unstructured research. Understanding where Claude and NotebookLM fit into this process helps product professionals see how generative AI can accelerate research synthesis while leaving interpretation, prioritization, and strategic decisions to the people responsible for the product.

Using Claude for User Research Synthesis and Problem Discovery

What if you could take 100 user reviews and turn them into clear themes, ranked problems, emotional patterns, and actionable insights in minutes instead of spending days manually analyzing them? In this lecture, you’ll see how Claude can help product teams accelerate the synthesis of large volumes of user research while maintaining the full context of the conversation.

Through a practical demonstration using 100 app onboarding reviews, you’ll see Claude cluster recurring complaints, rank themes by frequency, identify emotional tone, create structured problem statements, and provide representative examples. You’ll then build on that analysis through follow-up questions to form hypotheses about onboarding drop-off and segment complaints based on language patterns—all while treating AI outputs as hypotheses that require validation against real product and user data.

In This Lecture, You Will:

▸ Explore how Claude can process and synthesize a large collection of user reviews within a single conversation

▸ Learn how to cluster recurring complaints into clear themes and rank themes by frequency

▸ Analyze the emotional tone associated with different research themes

▸ Transform vague user complaints into structured, user-centric problem statements

▸ Use representative examples from reviews to support each identified theme

▸ Understand the difference between a vague observation and an actionable research insight

▸ Build on an initial analysis through follow-up questions while maintaining the context of the previous conversation

▸ Form a hypothesis about which issues may contribute to onboarding drop-off by considering both complaint frequency and emotional severity

▸ Understand why AI-generated hypotheses should be validated against real product analytics and behavioral data

▸ Explore how Claude can attempt to segment complaints based on language patterns and identify potential differences between user types

▸ Recognize the limitations of AI-generated segmentation and why these patterns should be validated through actual user research

Tools & Features Covered:

• Claude — used to analyze and synthesize 100 user reviews in a single conversation

Who This Lecture Is For:

• Product Managers and Product Professionals working with user feedback and product research

• UX Researchers and Product Researchers analyzing interviews, reviews, and qualitative feedback

• Product Designers turning user complaints into actionable problem statements

• Product Analytics and Insights Professionals connecting qualitative research with behavioral data

• Innovation and Strategy Professionals using research synthesis to develop product hypotheses

Why This Lecture Is Useful for Product Professionals

Product teams often have more qualitative feedback than they can efficiently analyze manually. This lecture shows how Claude can accelerate research synthesis, from identifying recurring themes to forming initial hypotheses, while keeping validation, interpretation, and strategic decisions with the product team.

Using NotebookLM for Multi-Source Product Research

What happens when the information you need to understand a product problem is scattered across interviews, support tickets, stakeholder notes, and competitor research? Reading each document separately can make it difficult to see the connections between them. In this lecture, you’ll see how NotebookLM brings these different research sources together so product teams can analyze them as one connected research corpus.

Through a practical demonstration, you’ll upload multiple research documents into a NotebookLM workspace, examine automatically generated source summaries, ask questions across all sources, and trace findings back to the exact source passages through citations. You’ll also explore how NotebookLM can surface cross-source themes, contradictions, evidence-backed prioritization, and additional research outputs.

In This Lecture, You Will:

▸ Understand the challenge of synthesizing product research that is distributed across multiple documents and sources

▸ Explore how NotebookLM brings multiple research documents into a single research workspace

▸ Learn how to create a notebook and organize research sources around a specific discovery question

▸ Explore the NotebookLM interface, including the Sources panel, chat interface, and output-generation options

▸ Upload research documents and understand how NotebookLM processes the sources

▸ Use automatically generated source summaries to quickly orient yourself to individual research documents

▸ Understand how NotebookLM creates a unified knowledge base from multiple uploaded sources

▸ Ask cross-source questions that connect themes, findings, and contradictions across different research documents

▸ Examine how citations connect AI-generated findings to the specific source document and passage they came from

▸ Identify cross-source consistency and tensions between different types of research evidence

▸ Explore how evidence from multiple sources can support a product prioritization hypothesis

▸ Understand why AI-generated findings should still be verified by checking the underlying source evidence and quantitative product data

▸ Explore additional NotebookLM outputs, including audio overviews, slide decks, video overviews, mind maps, reports, flashcards, quizzes, infographics, and data tables

▸ Understand how an Audio Overview can turn research findings into a conversational discussion that makes research easier to consume

Tools & Features Covered:

• NotebookLM — used for multi-source product research and synthesis

• Source Uploads — demonstrated with multiple research documents

• Source Summaries — demonstrated for quickly reviewing individual documents

• Source Citations — demonstrated for tracing findings back to specific source passages

• Audio Overview — demonstrated for turning research findings into a conversational audio discussion

• Google AI Studio / RAG — RAG is introduced as the underlying application, while its deeper coverage is reserved for later in the course

Who This Lecture Is For:

• Product Managers and Product Professionals working across multiple sources of user and market research

• UX Researchers and Product Researchers synthesizing interviews, support data, and other research documents

• Product Designers using research findings to inform design decisions

• Product Analytics and Insights Professionals connecting qualitative research across sources

• Innovation and Strategy Professionals working with cross-functional product research

Why This Lecture Is Useful for Product Professionals

Product decisions often require evidence from multiple sources rather than a single document. NotebookLM can bring those sources together, surface connections and tensions, and provide traceable citations so product teams can verify the evidence behind their findings before using them in design and prioritization decisions.

Building a Simple GenAI Application with APIs and Python

What happens behind the scenes when you type a prompt into a generative AI application and receive a response? In this lecture, you’ll move beyond the chat interface and see how an application can send a prompt to an AI model through an API and receive a response back.

Through a practical Python example in Google Colab, you’ll build a simple application that takes a product type or design challenge and generates a structured product design brief. You’ll see the complete flow from API authentication and user input to prompt templates, model selection, response parsing, and a clean final output.

In This Lecture, You Will:

▸ Understand what an API is and how applications can interact with AI models

▸ Learn how an API key identifies and authorizes requests made to an AI model

▸ Create an API key through Google AI Studio and understand why it should be treated like a password

▸ Explore Google Colab as an online environment for running Python code without installing Python locally

▸ Understand the basic flow of a GenAI application: user input → prompt → AI model → response → clean output

▸ Explore how a prompt template can structure instructions instead of requiring a complete prompt to be written for every request

▸ Understand the difference between the system message and the human message within the prompt template

▸ See how variables in a prompt template can automatically incorporate the user's input

▸ Use a product type such as a mental wellness app as input to generate a structured product design brief

▸ Understand how an AI model is selected and connected to the application

▸ Explore how the raw model response can contain technical information that is not intended for end users

▸ Understand how an output parser extracts the useful text from the model response

▸ See how the prompt template, model, and output parser can be connected into a single chain

▸ Experiment with different product types, including an AI-powered resume builder, to generate different product design briefs

Tools & Techniques Covered:

• Google AI Studio — used to create the API key and access Google AI models

• Google Colab — used to run the Python application

• Python — used to build the simple GenAI application

• Gemini Flash — used as the large language model in the application

• Prompt Templates — demonstrated for structuring system and user instructions with variables

• LangChain Output Parser — demonstrated for extracting clean text from the model response

• LangChain Chain — demonstrated for connecting the prompt template, model, and output parser into one flow

Who This Lecture Is For:

• Product Managers and Product Professionals who want to understand how GenAI applications work behind the scenes

• Product Designers and UX Professionals interested in building simple AI-assisted design tools

• AI and Technology Professionals exploring API-based interaction with language models

• Developers and Technical Professionals learning the basic structure of GenAI applications

• AI and Prompt Engineering Learners who want to move from using AI tools to understanding how applications connect to AI models

Why This Lecture Is Useful for Product Professionals

Understanding the basic architecture behind a GenAI application helps product professionals see how an AI feature moves from user input to a structured model response. This lecture provides a practical view of APIs, prompt templates, model interaction, and output parsing through a working product design brief application.

Advanced Product Data Analysis with AI

What if you could take your product design data, describe the analysis you need in plain English, and have AI write and execute the code, generate charts, and explain the results for you? In this lecture, you’ll see how advanced data analysis can be performed without manually writing code.

Using a product design dataset covering 500 features, you’ll work through three practical analyses: predicting feature abandonment, comparing multiple predictive models, and building a complete product analytics dashboard. The focus is not just on generating charts, but on understanding what the results mean and how they can inform product decisions.

In This Lecture, You Will:

▸ Analyze product design data using logistic regression to estimate the probability of feature abandonment

▸ Examine how factors such as usability scores, design iterations, user testing, and stakeholder change requests relate to feature abandonment

▸ Interpret a confusion matrix, coefficient chart, classification report, precision, recall, and F1 scores

▸ Build a decision tree to visualize how predictions are made

▸ Compare three predictive models and examine the relative importance of design factors across them

▸ Interpret model results using AI-generated explanations in plain English

▸ Create a multi-chart product analytics dashboard with headline KPIs, abandonment breakdowns, usability comparisons, scatter plots, and a correlation heat map

▸ Use plain-English prompts to request advanced analysis without manually writing the underlying code

▸ Improve analysis prompts by providing specific product context and asking AI to explain what the results mean for the product roadmap

▸ Apply follow-up questions to investigate surprising or unexpected findings

Tools & Techniques Covered:

• AI Data Analysis — demonstrated for analyzing uploaded product data and generating results

• Python Code Generation and Execution — AI writes and runs the analysis code automatically

• Logistic Regression — demonstrated for predicting feature abandonment

• Decision Tree — demonstrated for prediction and visualization

• Model Comparison — demonstrated across three predictive models

• Product Analytics Dashboard Generation — demonstrated through a multi-chart dashboard

Who This Lecture Is For:

• Product Managers who want to analyze product data and turn findings into actionable insights

• Product Designers who want to understand how design factors relate to product outcomes

• Product Analysts and Business Analysts interested in using AI to accelerate advanced analysis

• UX Researchers working with quantitative product and design data

• Innovation and Product Strategy Professionals who need to interpret data for product decisions

Why This Lecture Is Useful for Product Professionals

This lecture shows how AI can handle the technical work between a product question and the resulting analysis. By focusing on what questions to ask and what the results mean for the product, product professionals can use AI to explore predictive models, compare results, and create analytical dashboards without manually writing the underlying code.

Using Microsoft Copilot in Excel for Product Feedback Analysis

What if you could analyze product feedback in Excel without being an Excel power user? Instead of building pivot tables, writing conditional formulas, or cleaning inconsistent names manually, you can describe what you need in plain English and let Copilot handle the spreadsheet work.

In this lecture, you’ll work with a 120-response user feedback dataset and see how Microsoft Copilot in Excel can handle three practical product-data tasks: identifying unresolved feedback by product area, calculating how long feedback has remained open or took to resolve, and cleaning and tagging submitter names.

In This Lecture, You Will:

▸ Analyze open feedback items across product areas using a pivot table

▸ Create a pivot table with Copilot by describing the required analysis in plain English

▸ Calculate the number of days each feedback item has been open or took to resolve

▸ Apply different date logic for resolved and unresolved feedback items

▸ Generate the required Excel formula through a plain-English Copilot prompt

▸ Clean inconsistently formatted submitter names using Copilot

▸ Create a formatted submitter column that combines the user segment with the submitter's name in title case

▸ Use plain-English prompts instead of manually writing or memorizing Excel formulas

▸ Understand why product professionals still need to know which analysis to run and how to interpret the results, even when AI handles the spreadsheet work

Tools & Features Covered:

• Microsoft Copilot in Excel — demonstrated for analyzing and modifying product feedback data

• Edit with Copilot — used to allow Copilot to modify the Excel sheet

• Pivot Table Creation — generated through a natural-language prompt

• Formula Generation — Copilot creates formulas for row-level calculations

• Column Creation and Formatting — demonstrated for cleaning and tagging submitter names

Who This Lecture Is For:

• Product Managers who regularly analyze customer and user feedback

• Product Designers who work with feedback data but may not be Excel power users

• UX Researchers who need to organize and analyze feedback collected from multiple sources

• Junior Product Managers building confidence with product-data analysis

• Product Professionals who want to use AI to simplify routine spreadsheet analysis

Why This Lecture Is Useful for Product Professionals

This lecture shows how Microsoft Copilot in Excel can reduce the spreadsheet expertise required for common product-data tasks. By describing the desired analysis in plain English, product professionals can create summaries, calculations, and cleaned data while keeping their focus on understanding users, choosing the right analysis, and using the results for product decisions.

Retrieval-Augmented Generation (RAG): Grounding AI in Your Own Data

How can you make an AI system answer questions using your own trusted documents instead of relying only on what the language model already knows? This is the problem that Retrieval-Augmented Generation (RAG) is designed to address. In this lecture, you’ll understand the core idea behind RAG and why retrieving relevant information before generating an answer can improve the reliability and relevance of AI responses.

You’ll follow the complete conceptual flow of a RAG system, from preparing documents and creating chunks to generating embeddings, storing them in a vector database, retrieving relevant information, and using those retrieved chunks to generate a grounded response.

In This Lecture, You Will:

▸ Understand the core concept of Retrieval-Augmented Generation (RAG) and how it grounds AI responses in a trusted knowledge base

▸ Identify the limitations of relying only on a language model's existing knowledge when answering questions about internal information

▸ Explore why sending an entire document to a language model can increase cost, exceed context limits, and introduce irrelevant information

▸ Understand how documents are divided into smaller chunks so that relevant sections can be retrieved when needed

▸ Explore how intelligent chunking helps preserve context by keeping related paragraphs, sentences, or logical sections together

▸ Understand embeddings as numerical representations of the meaning and context contained within each chunk

▸ Explore how embeddings are stored and compared in a vector database

▸ Understand how a user's question is also converted into an embedding and compared with stored embeddings to identify relevant information

▸ See how RAG can retrieve information based on meaning and context, rather than relying only on exact keyword matches

▸ Follow the complete RAG flow from knowledge base → text extraction → chunking → embeddings → vector database → retrieval → grounded response

▸ Understand how LangChain can handle document reading, chunking, embedding generation, and vector database storage in a Python-based RAG workflow

▸ Recognize how RAG combines the reasoning capabilities of language models with information from an organization's own data

Tools & Techniques Covered:

• Retrieval-Augmented Generation (RAG) — the core approach for grounding AI responses in external knowledge

• LangChain — used to manage the RAG workflow and its document-processing steps

• Document Chunking — demonstrated conceptually for breaking large documents into manageable sections

• Contextual Embeddings — explained as numerical representations of meaning

• Vector Database — explained for storing and comparing embeddings based on similarity

• Similarity-Based Retrieval — used to identify the chunks most relevant to a user's question

Who This Lecture Is For:

• Product Managers who want to understand how AI applications can work with company-specific information

• Product Designers working with AI-powered products that need access to structured or unstructured knowledge

• AI and Product Professionals who want to understand the architecture behind grounded AI responses

• Developers and Technical Professionals exploring RAG-based AI applications

• Innovation and Strategy Professionals evaluating how organizational data can be used with generative AI

Why This Lecture Is Useful for Product Professionals

RAG is important for understanding how AI applications can work with organization-specific knowledge rather than relying only on general model knowledge. This lecture gives you a clear conceptual view of how documents are processed, relevant information is retrieved, and that information is provided to the language model to produce a more grounded and relevant response.

Practical RAG Implementation with Python and OpenAI

You’ve seen how RAG works in theory. Now what does it actually look like when you build one? In this lecture, you’ll create a working RAG system in Python and see how a document can be transformed into a searchable knowledge source that an AI model can use to answer questions.

Using a product design system document as the knowledge base, you’ll follow the complete implementation from document upload and chunking to embeddings, vector storage, retrieval, and grounded answer generation. By the end, you’ll see how a product team can turn its documentation into something people can query using plain English.

In This Lecture, You Will:

▸ Set up a Python-based RAG system using a provided Google Colab notebook

▸ Connect the application to the OpenAI API for embeddings and language-model interaction

▸ Load a product design system document as the RAG knowledge base

▸ Split the document into smaller chunks of approximately 1,000 characters with overlap to preserve context

▸ Create embeddings for each document chunk to represent its meaning in a form that can be compared computationally

▸ Store the embeddings in a Chroma vector store to make the design system searchable

▸ Convert a user's question into an embedding and retrieve the most relevant document chunks

▸ Inspect the retrieved chunks and see how they connect to the user's question

▸ Build a retrieval pipeline that places the relevant chunks into a prompt template

▸ Instruct the language model to answer using the retrieved context rather than making unsupported assumptions

▸ Generate a grounded answer to a product design question using the information contained in the design system

▸ Understand how the same approach can make product documentation such as UX research, product requirements, brand guidelines, and accessibility standards searchable

Tools & Techniques Covered:

• Python & Google Colab — used to build and run the RAG implementation

• OpenAI API — used for the embedding model and language model

• LangChain — used to build the RAG workflow

• Document Chunking — demonstrated for breaking the design system into manageable sections

• Embeddings — demonstrated for representing document chunks by their meaning

• Chroma Vector Store — used to store and search the document embeddings

• Retrieval — demonstrated for finding the most relevant document chunks

• Prompt Templates — used to provide retrieved context and instructions to the language model

• Grounded Generation — demonstrated by generating an answer based on the retrieved design-system content

Who This Lecture Is For:

• Product Managers who want to understand how RAG can make product documentation searchable

• Product Designers who work with design systems, UX guidelines, and product documentation

• AI Product Professionals exploring practical RAG implementations

• Developers and Technical Professionals learning how to build RAG workflows with Python

• UX and Product Researchers working with large collections of research and documentation

Why This Lecture Is Useful for Product Professionals

This lecture turns the concepts from the previous RAG discussion into a working implementation. By seeing how a design system is chunked, embedded, stored, retrieved, and passed to an AI model, you can understand how product teams can build AI experiences that answer questions using their own documentation rather than relying only on general model knowledge.

Running Large Language Models Locally with Ollama

What if your team could use an AI model without sending sensitive company information to an external server? In this lecture, you’ll explore how local LLMs can run directly on your own computer, keeping data within your system while allowing you to interact with an AI model in a familiar chat-based way.

You’ll set up Ollama, download and run a local model, and test it with a product-management use case. You’ll also explore Ollama’s model catalog and understand how model size, parameters, performance, and computing requirements affect which model you can run locally.

In This Lecture, You Will:

▸ Understand why organizations may consider running LLMs locally for privacy and internal data use

▸ Explore how local models can operate without depending on external AI servers

▸ Install Ollama on a computer and understand the basic setup process

▸ Download a local AI model through the Ollama interface

▸ Interact with a locally running model through Ollama's user interface

▸ Generate a product-related output by asking the model to create a user story for a mobile onboarding flow

▸ Observe how local model performance and generation speed depend on the available computing power

▸ Test the local model without an internet connection after the model has been downloaded

▸ Explore the Ollama model catalog and identify different open-source models available to run locally

▸ Understand how model parameter sizes such as 8B, 70B, and 405B relate to model scale

▸ Compare the relationship between model size, storage requirements, response speed, and computing power

▸ Understand how organizations can select a model based on the hardware available to them

Tool Covered:

• Ollama — used to install, download, and interact with local LLMs

Who This Lecture Is For:

• Product Managers who need to understand options for using AI with sensitive product or organizational information

• Product Designers exploring private AI workflows for product work

• AI Product Professionals interested in local and open-source language models

• Technology and Innovation Professionals evaluating locally deployed AI options

• Developers and Technical Professionals interested in running LLMs on local systems

Why This Lecture Is Useful for Product Professionals

This lecture provides a practical introduction to local AI deployment and shows how a product professional can interact with an LLM without relying on an external AI service for every interaction. Understanding local models, model sizes, and hardware requirements helps teams evaluate how AI can fit into workflows where privacy and control over data are important.

Product Insights Through Prompt Engineering

What if the prompts your design team writes could reveal problems within the product itself? Generative AI interactions are not only a way to create ideas or refine designs—they can also provide signals about the challenges designers are repeatedly trying to solve.

In this lecture, you’ll explore how analyzing AI prompts and interactions can uncover recurring product design challenges, identify areas of UX friction, and support better design decisions. You’ll also see how teams can turn these insights into practical improvements, design guidance, and product roadmap priorities.

In This Lecture, You Will:

▸ Understand how designers’ prompts and AI interactions can reveal valuable information about the product challenges they are trying to solve

▸ Identify recurring patterns in prompts related to common design challenges such as onboarding, activation, and first-time user experience

▸ Recognize how frequent prompts about the same product area can signal potential UX friction or design problems

▸ Analyze how prompt patterns can provide signals about challenges designers are facing during the product design process

▸ Use prompt-derived insights to identify opportunities for improving the user experience

▸ Apply these insights to areas such as simplifying onboarding and reducing sign-up friction

▸ Introduce clear design guidelines and reusable design patterns to help teams address recurring UX challenges

▸ Use insights from AI interactions to identify broader product issues and inform design improvement priorities

▸ Understand how analyzing generative AI interactions can support better design decisions and product experiences

Who This Lecture Is For:

• Product Managers who want to identify product and UX issues through design-team activity

• Product Designers who want to recognize recurring design challenges and improve user experiences

• UX Designers and Researchers analyzing patterns in design work and user journeys

• Product Leaders who need to identify broader product issues and prioritize design improvements

• Design and Product Teams looking for additional signals to guide UX improvements

Why This Lecture Is Useful for Product Professionals

This lecture shows how AI interactions can become a source of product insight. By recognizing recurring patterns in the prompts designers use, teams can identify potential UX friction, improve design guidance, and surface broader product issues that may deserve attention in the product roadmap.

Hallucination & Bias in Generative AI

What happens when an AI-powered product confidently gives users biased assumptions or completely incorrect information? When generative AI becomes part of the design process, its outputs can influence personas, UX copy, feature prioritization, accessibility decisions, and other product experiences at scale. That makes understanding bias and hallucination an essential part of responsible product design.

In this lecture, you’ll explore how bias can narrow the users represented in AI-generated outputs and how hallucinations can introduce fabricated information into product decisions. You’ll also examine four practical strategies for reducing these risks: inclusive inputs, human review, continuous monitoring, and Retrieval-Augmented Generation (RAG) for grounding AI in verified design knowledge.

In This Lecture, You Will:

▸ Understand how bias in training data can influence AI-generated personas, UX patterns, onboarding recommendations, and other design outputs

▸ Recognize how AI bias can overlook different demographics, abilities, geographies, and user contexts

▸ Examine how biased AI outputs can influence design assumptions, accessibility decisions, and the users a product ultimately serves

▸ Understand hallucination and how AI can present fabricated information with confidence

▸ Explore how incorrect AI-generated benchmarks or accessibility guidance can lead to flawed product decisions and outcomes

▸ Apply inclusive input design by deliberately requesting diverse user representations and testing AI-generated outputs across different user groups

▸ Build human review into the design workflow before AI-generated personas, benchmarks, accessibility guidance, or UX content influence important decisions

▸ Monitor AI outputs continuously and use user feedback to identify outputs that are incorrect, biased, or confusing

▸ Understand why AI quality should be treated as an ongoing product responsibility rather than a one-time launch check

▸ Use Retrieval-Augmented Generation (RAG) to ground AI responses in verified design knowledge such as design-system documentation, validated research findings, and established UX guidelines

▸ Recognize the role of product designers and managers in connecting AI capability with user trust and accountability

Tools & Techniques Covered:

• Inclusive Input Design — using deliberate prompts and context to encourage diverse user representations

• Human Review — incorporating validation into AI-assisted design workflows

• AI Output Monitoring — continuously reviewing AI-generated outputs and user feedback

• Retrieval-Augmented Generation (RAG) — grounding AI responses in curated and verified design knowledge

Who This Lecture Is For:

• Product Managers making decisions with AI-generated insights, recommendations, or content

• Product Designers using generative AI for personas, UX patterns, copy, and design decisions

• UX Researchers evaluating AI-generated user representations and research-related outputs

• AI Product Professionals responsible for designing AI-powered product experiences

• Product Leaders establishing responsible practices for AI-assisted product development

Why This Lecture Is Useful for Product Professionals

This lecture helps product teams recognize how bias and hallucination can affect AI-assisted design decisions and provides practical ways to address them. By combining inclusive inputs, human oversight, continuous monitoring, and grounded AI architectures such as RAG, teams can build AI-powered experiences with greater attention to accuracy, fairness, and user trust.