Google Generative AI Foundations
FoundationTwo days on how generative AI actually works and where every piece of the Google stack fits — Gemini, AI Studio, Vertex AI, NotebookLM — plus responsible AI and real use cases.
Program Fee
1-on-1 doubt clearing · WhatsApp group · Post-training Q&A
Request Syllabus / Call BackProgram Overview
Two days that give you a complete, structured picture rather than a tour of features. Day one is how generative AI actually works — models, tokens, context, prompting and its limits, without mathematics — and then the Google stack laid out honestly: Gemini, AI Studio for prototyping, Vertex AI for enterprise, NotebookLM for grounded research, and exactly where each one belongs. Day two moves to practice: improving output through grounding and RAG, Google’s responsible AI principles applied to real decisions rather than recited, and a full module of business use cases across functions. You finish by scoping and defending a generative AI use case for your own organisation.
What You'll Learn
- Explain how generative AI and large language models work, without mathematics
- Describe tokens, context windows and why the model forgets
- Map the Google AI stack and place each product correctly
- Choose between AI Studio and Vertex AI on evidence
- Use NotebookLM for grounded research
- Improve output through prompting, grounding and RAG
- Apply Google’s responsible AI principles to a real decision
- Recognise hallucination, bias and hard limits
- Identify and scope generative AI use cases across business functions
- Build and defend a business case for a generative AI initiative
Who Can Join
- •Executives and managers
- •Business analysts and project managers
- •IT executives evaluating the Google stack
- •Anyone who needs the whole picture, not a feature tour
PREREQUISITES
• None
• No coding required
Curriculum
→ What a model is
→ Tokens and context
→ Training and the cutoff
→ Why answers vary
→ Lab 0
Total: 14h across 8 modules
Tools & Technologies
Projects Included
- ▹ Lab 0 — Model behaviour probed: tokens, context and consistency
- ▹ Lab M01 — The Google stack mapped to three real scenarios
- ▹ Lab M02 — A grounded research workflow built in NotebookLM
- ▹ Lab M03 — Prototyping in AI Studio
- ▹ Lab M04 — Grounding and RAG applied to your own content
- ▹ Lab M05 — Responsible AI applied to a real decision
- ▹ Lab M06 — Use cases identified across business functions
- ▹ Lab M07 — A generative AI business case, built and defended
Official Certifications
Assessment is continuous and practical: 8 Applied Learning Labs (60%) and an end-of-course practical assessment (40%). Passing mark 80%, plus completion of all 8 labs and full attendance. The CertTulen Course Completion Certificate is awarded on meeting that standard.
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