7-Day Study Plan for Generative AI Leader Certification

Designed for maximum effectiveness using Pomodoro + Spaced Repetition

Method Overview

• Pomodoro Technique: 1 Pomodoro = 25 min focused study + 5 min break. Every 4 Pomodoros = 30 min long break.

• Spaced Repetition: Revise key content on Day 1, Day 3, and Day 6 to combat memory decay.

• Each day includes specific goals, clearly defined tasks, and study methods.

Day 1 – Foundations of Generative AI

Goal: Understand what Generative AI is, how LLMs work, and key architectural concepts like Transformers.

Tasks:

1. Read foundational materials on Generative AI vs Traditional AI.

   • Take notes on differences in structure, outputs, and learning style.

2. Study LLM architecture (Transformer model, self-attention mechanism).

   • Draw a diagram of the Transformer flow.

3. Compare foundation models: GPT, PaLM, Gemini, Claude.

   • Create a comparison table highlighting purpose, strengths, and use cases.

4. Summarize training techniques: Pretraining vs. fine-tuning vs. prompt-tuning.

   • Write a 1-paragraph summary of each method in your own words.

Study Methods:

• Pomodoro 1: Reading and note-taking

• Pomodoro 2: Visual mapping (Transformer architecture)

• Pomodoro 3: Comparison table (model characteristics)

• Pomodoro 4: Writing summaries and flashcard creation

Day 2 – Prompt Engineering Techniques

Goal: Master prompt formats, styles, and design strategies to control model outputs.

Tasks:

1. Learn prompt types: Zero-shot, Few-shot, Chain-of-thought (CoT).

   • Create a worksheet with example prompts and their ideal use cases.

2. Experiment with prompt styles:

   • Test different formats using a GenAI playground (e.g., PaLM or Gemini).

   • Record results and compare variations.

3. Study output control parameters: Temperature, Top-k, Top-p, Max Tokens.

   • Write a summary table with recommended settings per task type.

4. Practice prompt refinement:

   • Rewrite 3 low-quality prompts into high-quality ones using role, format, structure.

Study Methods:

• Active testing + prompt debugging

• Use Google tools or public LLMs for experimentation

• Flashcard making for terminology

Day 3 – Google Cloud GenAI Products

Goal: Learn the core GenAI tools from Google Cloud and understand when and how to use each.

Tasks:

1. Study Vertex AI:

   • Identify components: model access, prompt interface, customization, experiment tracking.

   • Create a diagram of how data flows through Vertex AI.

2. Explore Generative AI Studio:

   • List 3 use cases (summarization, chatbot, image generation).

   • Try out one task using the no-code interface (if available).

3. Understand Model Garden:

   • List 5 models from different sources (Google, open-source, third-party).

   • Identify customization and deployment options.

4. Learn about Codey & Imagen APIs:

   • Write down examples of code generation, explanation, and image creation.

Study Methods:

• Tool comparison tables

• Real or simulated hands-on sessions

• Diagram creation for workflows

Day 4 – Improving Output Quality

Goal: Master advanced prompt tuning, context management, RAG, and evaluation techniques.

Tasks:

1. Study conversation memory management:

   • Learn token limits and strategies to compress, summarize, and manage sessions.

2. Practice multi-turn prompt design:

   • Build a prompt chain for a customer support assistant with 3 back-and-forth turns.

3. Learn RAG process:

   • Write out the 4-step flow: vectorize → retrieve → inject → generate.

   • Visualize the process in a flowchart.

4. Explore evaluation tools:

   • Research BLEU, ROUGE, F1 scores.

   • Run a prompt and evaluate it using manual and metric-based methods.

Study Methods:

• Visual aids (flowcharts and tables)

• Prompt writing exercises

• Peer-like review (self-check and score)

Day 5 – Business Strategy and Real-World Applications

Goal: Learn to identify high-impact use cases, structure pilots, and align with ethical AI principles.

Tasks:

1. Identify suitable use cases:

   • Fill a matrix: business area → task type → value opportunity.

2. Learn pilot-to-production pathway:

   • Write out the 5 stages: Prototype → Pilot → Measure → Iterate → Scale.

3. Study Responsible AI principles:

   • Create a chart with Fairness, Transparency, Privacy, Accountability — and a risk per each.

4. Practice scenario analysis:

   • Given a use case (e.g., automating legal contracts), write a deployment plan including compliance and security.

Study Methods:

• Case-based learning (simulate workplace settings)

• Table-based frameworks for quick comparison

• Scenario writing for transfer application

Day 6 – Full Review + Memory Reinforcement

Goal: Review and reinforce key concepts, identify knowledge gaps using quizzes, and refresh flashcards.

Tasks:

1. Review flashcards from Days 1–5 (use apps like Quizlet).

2. Redraw all diagrams: Transformer, RAG, Vertex AI workflow, Pilot-to-Production.

3. Take a mini mock test of 20–25 questions across all topics.

4. Analyze errors and weak areas, go back to content as needed.

Study Methods:

• Spaced repetition via flashcards

• Concept recall exercises

• Active test review

Day 7 – Final Simulation and Mastery Output

Goal: Simulate a real exam, output your knowledge, and enter exam day with confidence.

Tasks:

1. Take a full 45–60 min practice test under timed conditions.

2. Review and reflect:

   • For every missed question, trace it back to its related module.

3. Summarize the 4 core domains:

   • Write 1-paragraph explanations for each of the exam's main domains.

4. Teach the material:

   • Record yourself explaining GenAI to a beginner in 5 minutes (Feynman technique).

Study Methods:

• Simulation + reflection

• Output-based review (writing or speaking)

• Confidence-building through teaching

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