Google Cloud Generative AI LeaderStudy guide

Understand the nesting of artificial intelligence (AI), machine learning (ML), deep learning, and generative AI; the learning types (supervised, unsupervised, reinforcement); structured vs unstructured data; and modalities such as text, image, audio, and multimodal—from a business perspective.

Understand the idea and generality of foundation models, how large language models (LLMs) predict the next token using tokens and prompts, the difference between pretraining and fine-tuning, terms like parameters and context window, and the basics of embeddings—from a business perspective.

Learn the limitations and risks to understand when using generative AI in business: hallucinations (plausible but wrong outputs), bias from training data, knowledge cutoff, output uncertainty and reproducibility, the difficulty of explainability, and data privacy, copyright, and security.

Understand Google core generative AI model Gemini (multimodal), modality-specific models such as Imagen (image), Veo (video), and Chirp (audio), Model Garden for choosing among foundation models, and Vertex AI and Vertex AI Studio as the platform to build and use AI.

Understand generative AI solutions you embed directly into work: Gemini in Google Workspace (drafting documents, email, meetings), Gemini for Google Cloud for development and operations, NotebookLM for research and summarization, and the Customer Engagement Suite / Conversational Agents (CCAI) for automating customer service.

Understand agents (agentic AI) that autonomously execute tasks toward a goal and Vertex AI Agent Builder to build them, grounding that bases answers on Google Search or your own data, and the role of the AI infrastructure (TPUs/GPUs) that powers generative AI—from a business perspective.

Understand prompt techniques that improve output quality: the basics of giving clear instructions, context, and an expected output format; zero-shot vs few-shot (no examples vs examples); assigning a role (persona); prompting step-by-step reasoning; and tuning output randomness with temperature.

Understand how to choose among key techniques to improve output: grounding and RAG (retrieval-augmented generation) to answer accurately from your own data, embeddings and vector search for semantic retrieval, fine-tuning to adapt the model to a domain, function calling (tools) to invoke external systems, and evaluation to measure output quality.

Understand how to adopt generative AI safely and responsibly: responsible AI under Google AI principles (fairness, transparency, accountability, privacy, human-centeredness), the Secure AI Framework (SAIF) for AI-specific threats, handling of input data and data governance, and human-in-the-loop for critical decisions.

Understand strategies to turn generative AI adoption into success and business value: identifying promising use cases and return on investment (ROI), moving from pilot to production, upskilling and change management, cost and sustainability, and leveraging partners and the ecosystem.