AI Terminology for Designers & PMs

Most designers and product managers are expected to work with AI but were never taught what it actually means. When an engineer talks about fine-tuning a model, or a product brief mentions RAG and hallucination, or a stakeholder asks about alignment, the pressure to nod along is real. That gap between expectation and vocabulary costs teams time, creates miscommunication, and keeps product professionals out of decisions that directly affect their work.
AI Terminology bridges that gap. The course covers how artificial intelligence works at a conceptual level: what machine learning is, how it differs from traditional software, and why neural networks behave the way they do. From there, it moves into the mechanics of large language models, including what tokens and embeddings are, why models hallucinate, and what prompt engineering actually does and does not control. These are the terms that come up daily in modern product work, and understanding them changes how confidently designers and PMs can read documentation, write requirements, and push back on vague technical claims.
The course also covers the vocabulary of AI in product contexts: agentic systems, human-in-the-loop design, explainable AI, and adaptive interfaces. Knowing these terms helps designers ask sharper questions about AI feature proposals and helps product managers scope work without having to defer entirely to engineering. The final section addresses responsible AI: fairness, bias, transparency, accountability, and governance. These concepts are no longer optional for product teams. Regulations like the EU AI Act and growing public scrutiny of AI-powered products mean that everyone shaping a product needs a working vocabulary for ethical AI decision-making, not just engineers.