Joe Natoli

    GenAI Fundamentals for UX Designers + Researchers

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    Figma · Online Course

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    Unlock the future of user experience with 'GenAI Fundamentals for UX Designers + Researchers,' an immersive course led by veteran UX strategist Joe Natoli. As artificial intelligence continues to reshape the digital landscape, product designers and researchers must evolve from traditional wireframing to guiding complex, machine-learning-driven systems. This course acts as your definitive blueprint for understanding how Generative AI operates under the hood, empowering you to lead AI product innovation instead of just reacting to it. You will dive deep into critical concepts such as distinguishing between automation and augmentation, balancing precision and recall, and navigating the complexities of generative variability. Joe Natoli guides you through practical design frameworks, including how to structure user-facing confidence scores, implement value-sensitive design, and manage the inevitable imperfections of AI-generated outputs. By mastering these core principles, you will be uniquely positioned to advocate for user trust, equity, and transparency in any AI-driven product team. Whether you are aiming to design more ethical, unbiased interfaces or want to command high-level strategic conversations with engineers, this program equips you with the vocabulary and methodologies needed to design safe, delightful, and highly functional AI-augmented experiences.

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    Joe Natoli

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    Design

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    Joe Natoli

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    Joe Natoli is a veteran User Experience (UX) consultant, author, and speaker with over three decades of real-world experience. He has spent his career helping Fortune 500 companies, startups, and government organizations design products that solve complex user problems while driving business growth. His teaching style is famously direct, practical, and devoid of academic jargon. Joe focuses on "the why" and "the how" of design, ensuring that students do not just learn tools but master the strategic, critical-thinking skills required to survive and thrive in the industry. Across his massive catalog of courses—ranging from Information Architecture and UX Audits to…Show more

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    Program Overview

    Online Course

    Learning format

    Figma

    Subcategory

    $49.99

    Price

    Price may change · updated within 1–2 weeks

    English

    Course language

    What You'll Learn

    • Learn core principles of responsible GenAI design that ensure our products serve all users equally + equitably.
    • Success factors unique to Machine Learning (ML) products that UX + Product Designers and their teams should adopt
    • Guidance on research to determine what kinds of problems are best solved by AI, and where human control should remain central.
    • Determining when AI features are appropriate for users — and when they aren't
    • Identifying when Automation (AI does the task for users) or Augmentation (help them do it better) is more appropriate.
    • Designing the reward function and appropriately considering the balance between false positives and false negatives.
    • Essential factors to consider when evaluating the reward function.
    • How to weigh necessary, unavoidable tradeoffs between precision and recall, which is key to shaping an AI user experience.
    • Designing for fairness and inclusion, from objectives to datasets to guidance on bias testing.
    • Designing for generative variability: how do we present multiple, varied outputs to users — and how do we guide them in selecting the best one?
    • Designing for multiple outputs: how do we help users filter and highlight differences between outputs?
    • Designing for imperfections: how do we empower users to manage + mitigate imperfections and designing with contextual sensitivity?
    • Designing for confidence: how do we design confidence scoring to properly evaluate output quality and increase user trust?
    • Rules and examples for applying confidence scores.
    • Designing for co-creation: how to we design co-creation processes where both the user and the AI can make adjustments?
    • Designing for generic controls: using "temperature" to control the number of outputs and the degree of variability in those outputs.
    • Designing for domain-specific controls, such as encoder-decoder models, semantic sliders and prompt engineering.
    • Designing for prompt engineering: enabling and guiding users to effectively use multiple types of conversational prompts.
    • Designing for exploration: incorporating flexibility, feedback, transparency and error handling/expectation management.
    • Designing for choice, feedback, transparency + safety: centering users as active, empowered participants in the creation process.
    • Designing for mental models: orienting users to generative variability, teaching effective use and teaching the AI about the user.
    • Designing for explanation, understanding + trust: providing clear rationales for outputs, using friction to curb over-reliance and showing imperfections.
    • Designing against harm: understanding the critical ways irresponsible AI design can harm people.
    • Designing against hazardous outputs: discrimination, exclusion, toxicity, misinformation, deep fakes, IP theft and more.
    • Mitigating harm with a Value-Sensitive Design (VSD) process, integrated with an Agile or Lean development process.

    Best For

    • UX designers and researchers seeking to integrate Generative AI capabilities into their digital products.
    • Product managers needing a strategic framework for navigating AI-driven design tradeoffs like precision vs. recall.
    • Designers aiming to master ethical AI implementation through Value-Sensitive Design and bias mitigation.
    • Professionals who want to move beyond basic wireframing to designing complex, human-centric machine learning interfaces.

    Not For

    • Beginners looking for a technical course on how to program, train, or code AI machine learning models.
    • Individuals seeking a guide purely on how to use AI tools for automated graphic design or visual asset generation.
    • Product designers who are not interested in the strategic, research-heavy side of product development.

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