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Generative AI Leader Study Plan

Three weeks at 4-5 hours per week. Shorter than most plans here, because there is no lab work and the technical depth is deliberately limited.

Week 1: Fundamentals

  • Download the exam guide and the official study guide
  • Start the free Cloud Skills Boost learning path
  • AI, machine learning, deep learning, and generative AI: how they nest
  • Foundation models, and how they differ from task-specific models
  • Tokens, context window, and how both drive cost
  • Embeddings and semantic similarity
  • Multimodal models: text, image, audio, video
  • Pre-training, fine-tuning, and instruction tuning
  • Hallucination, its causes, and grounding as the mitigation
  • Agents and tool use at a conceptual level
  • Model limitations: knowledge cutoff, reasoning limits, bias, non-determinism
  • Review Notes: notes/01-genai-fundamentals.md

Week 2: Google Cloud offerings and techniques

  • Vertex AI: Model Garden, Vertex AI Studio, training, deployment, evaluation
  • Gemini model family and multimodality
  • Gemma open models; Imagen, Veo, Chirp
  • Vertex AI Agent Builder and Conversational Agents
  • Vertex AI Search for grounded enterprise search
  • Gemini for Google Workspace and Gemini for Google Cloud
  • NotebookLM
  • BigQuery generative AI functions
  • AI Hypercomputer, TPUs, GPUs
  • Build versus buy: the ladder from applied assistant to custom model
  • Prompt engineering: zero-shot, few-shot, chain-of-thought, system instructions
  • Parameters: temperature, top-k, top-p, output length
  • Grounding with Google Search and with enterprise data; RAG
  • Fine-tuning, and when it is worth the cost
  • Function calling and extensions
  • Evaluation approaches and safety filters
  • Review Notes: notes/02-google-cloud-offerings.md and notes/03-improving-output.md

Week 3: Business strategy and review

  • Identifying valuable use cases, and recognizing ones that do not need AI
  • Prioritizing by value and feasibility
  • Building the business case and defining success measures
  • Total cost of ownership beyond inference
  • Data readiness as the usual real constraint
  • Change management, training, and adoption
  • Responsible AI: fairness, transparency, explainability, privacy, safety, accountability
  • Google's AI Principles and the Secure AI Framework
  • Risks: hallucination in customer-facing use, IP, data leakage, regulation
  • Human in the loop, and where automation is inappropriate
  • Review Notes: notes/04-business-strategy.md
  • Two timed practice exams

Readiness check

  • Explain what a foundation model is and why it changed the economics of AI
  • Explain grounding and how it reduces hallucination
  • Choose between grounding, RAG, and fine-tuning for a stated requirement
  • Name the Google product for enterprise search, conversational agents, and image generation
  • Explain when to use an applied assistant rather than building on the platform
  • Describe how you would measure whether a generative AI project succeeded
  • List Google's responsible AI themes and give a control for each
  • Name a use case that sounds like AI but should be solved another way