Google Cloud Generative AI Leader (GAIL)

A free, self-paced 15 module training curriculum for Google Cloud Generative AI Leader (GAIL), built for business and technical leaders adopting generative AI on Google Cloud. Completing every module issues a printable 15.0 contact hour certificate from TECHLEAD 187 LLC.

Domains covered

Module syllabus

  1. 1.1 Gen AI Concepts & Use Cases
    Defining core gen AI concepts, foundation models, and business use cases across text, image, code, and video.
  2. 1.2 Data Types & Business Implications
    Describing structured/unstructured data, labeled/unlabeled data, and the importance of data quality.
  3. 1.3 Gen AI Landscape Layers
    Identifying infrastructure, models, platforms, agents, and application layers of the gen AI ecosystem.
  4. 1.4 Google Foundation Models
    Use cases and strengths of Gemini, Gemma, Imagen, and Veo.
  5. 2.1 Google Cloud Strengths
    AI-first approach, enterprise-ready platform, comprehensive ecosystem, and open approach.
  6. 2.2 Prebuilt Gen AI Offerings
    Business value of Gemini app, Gemini Advanced, Gemini Enterprise, and Workspace integration.
  7. 2.3 Improving Customer Experience
    Vertex AI Search and Customer Engagement Suite (Conversational Agents, Agent Assist).
  8. 2.4 Empowering Developers
    Vertex AI Platform, Model Garden, and RAG offerings for building with AI.
  9. 2.5 Tooling for Gen AI Agents
    extensions, functions, data stores, and relevant pre-built AI APIs (Speech-to-Text, Document AI).
  10. 3.1 Overcoming Model Limitations
    Addressing data dependency, bias, and hallucinations via grounding, RAG, and HITL.
  11. 3.2 Prompt Engineering Techniques
    Zero-shot, few-shot, role prompting, prompt chaining, and Chain-of-Thought techniques.
  12. 3.3 Grounding Techniques
    Differentiating world data vs enterprise data grounding and implementing web-based verification.
  13. 4.1 Implementing Gen AI Solutions
    Choosing right solutions, integration steps, and measuring impact of AI initiatives.
  14. 4.2 Secure AI & Protection
    Protecting systems via SAIF, IAM, and Security Command Center.
  15. 4.3 Importance of Responsible AI
    Privacy risks, anonymization, accountability, and explainability in AI systems.

What each module includes

An executive lesson briefing with five key concepts and official vendor documentation references, a scenario based knowledge check scored for mastery, an applied scenario evaluation, and supplemental verified videos plus official free hands on labs where available.

Start this curriculum

Other curricula

Published by TECHLEAD 187 LLC. Training certificates evidence completed instruction and contact hours; they are not vendor certifications and do not by themselves satisfy technical compliance requirements. Not affiliated with or endorsed by Google, Amazon Web Services, Microsoft, ISC2, Anthropic, or any U.S. government agency.