curriculum

8-Week AI Mastery Curriculum

A comprehensive, step-by-step program designed for busy professionals to quickly gain practical AI skills

Program Structure

Each week will focus on a Theme with specific outcomes.
There will be video lectures covering topics outlined below.
Each week, there will be specified Exercises for practicing with detailed instructions.
Students are expected to read required & additional readings.
Students are expected to submit assignments before starting new week lessons.
Quiz or Assessment - will be there based on the topics covered.
Project Activity - apply what is learnt for the week and this project will run through the program with a final submission.

Weekly Course Schedule

Week 1: Orientation, Goal-Setting & AI Foundations

Outcomes:

Quick orientation and goal setting for the Program. Build a solid foundation in generative AI concepts. Identify key professional tasks where AI can save time or add value.

Topics Covered:

Lesson 1.1:

Orientation, Goal-Setting
Lesson 1.2:

Generative AI Fundamentals: What generative AI is (large language models, etc.) and how it works at a high level – explained in non-technical terms.
Lesson 1.3:

AI Tools Landscape: Overview of today's top generative AI tools for knowledge workers. ChatGPT, Claude, and Google's Gemini are introduced
Lesson 1.4:

Setting Up for Success: Ensure accounts/access to chosen AI tools. Best practices for a productive AI workflow

Week 2: Prompt Engineering Fundamentals

Outcomes:

Learners will be able to construct clear and effective prompts using fundamental techniques, understand how LLMs process prompts, and begin iteratively refining their prompts for better results. They will apply these fundamentals to their course project.

Topics Covered:

Lesson 2.1:

Anatomy of a Prompt & How LLMs "Think"
Lesson 2.2:

The Art of Clarity: Writing Instructions LLMs Understand
Lesson 2.3:

Guiding with Examples: Zero-Shot, One-Shot, and Few-Shot Prompting
Lesson 2.4:

Iterative Prompt Development & Basic Debugging

Week 3:Intermediate Prompt Engineering

Outcomes:

Learners will be able to craft more nuanced prompts, effectively manage larger contexts, control LLM tone and style with greater precision, and begin to chain prompts for multi-step tasks. They will apply these intermediate techniques to further develop their course project.

Topics Covered:

Lesson 3.1:

Mastering Context: Window Size, Summarization, and Information Retrieval
Lesson 3.2:

Controlling Tone, Style, and Voice
Lesson 3.3:

Structuring Output: JSON, Markdown, Tables, and Other Formats
Lesson 3.4:

Week 4: Orientation, Goal-Setting & AI Foundations

Outcomes:

Learners will be able to implement advanced techniques like Chain-of-Thought, Self-Consistency, and Tree-of-Thoughts prompting, understand how to generate and evaluate multiple responses, and begin to explore techniques for reducing bias and improving factuality. They will apply these advanced methods to tackle more complex aspects of their course project.

Topics Covered:

Lesson 4.1:

Chain-of-Thought (CoT) Prompting: Guiding LLM Reasoning
Lesson 4.2:

Self-Consistency: Improving Accuracy by Sampling Multiple CoTs
Lesson 4.3:

Tree-of-Thoughts (ToT) / Graph-of-Thoughts (GoT) Prompting (Conceptual)
Lesson 4.4:

Reducing Hallucinations and Improving Factuality

Week 5: Use Case Specific Prompt Engineering

Outcomes:

Learners will be able to develop highly effective prompts for a variety of common business applications, understanding the nuances and best practices for each type of task. They will apply these specialized techniques to refine the core use cases of their course project.

Topics Covered:

Lesson 5.1:

Advanced Summarization Techniques
Lesson 5.2:

Prompting for Content Creation & Transformation
Lesson 5.3:

Effective Question Answering (Q&A) Prompts
Lesson 5.4:

Prompts for Data Extraction and Simple Analysis

Week 6: Ideation, Brainstorming, & Research

Outcomes:

Learners will master advanced prompting techniques to use LLMs as powerful partners in creative problem-solving, comprehensive research, and the critical evaluation of complex information, enabling them to drive innovation and generate high-quality insights in their professional roles.

Topics Covered:

Lesson 6.1:

Advanced Ideation & Creative Problem Solving with LLMs
Lesson 6.2:

Structured Brainstorming & Idea Evaluation with LLMs
Lesson 6.3:

LLM-Powered Research Strategies & Advanced Synthesis
Lesson 6.4:

Critical Thinking & Ethical Considerations in LLM-Driven Innovation

Week 7: AI-Powered Content, Communication, Collaboration & Influence

Outcomes:

Topics Covered:

Lesson 6.1:

Advanced Ideation & Creative Problem Solving with LLMs
Lesson 6.2:

Structured Brainstorming & Idea Evaluation with LLMs
Lesson 6.3:

LLM-Powered Research Strategies & Advanced Synthesis
Lesson 6.4:

Critical Thinking & Ethical Considerations in LLM-Driven Innovation

Week 8: AI for Meta-Learning — Master Any New Skill with Enhanced Efficiency

Outcomes:

Learners will be able to design and implement LLM-assisted meta-learning strategies, including creating personalized learning plans, rapidly acquiring and comprehending information, practicing skills with AI feedback, and iteratively adapting their learning approaches for significantly enhanced efficiency and mastery.

8-Week AI Mastery Curriculum

Program Structure

Weekly Course Schedule

Week 1: Orientation, Goal-Setting & AI Foundations

Outcomes:

Topics Covered:

Week 2: Prompt Engineering Fundamentals

Outcomes:

Topics Covered:

Week 3:Intermediate Prompt Engineering

Outcomes:

Topics Covered:

Week 4: Orientation, Goal-Setting & AI Foundations

Outcomes:

Topics Covered:

Week 5: Use Case Specific Prompt Engineering

Outcomes:

Topics Covered:

Week 6: Ideation, Brainstorming, & Research

Outcomes:

Topics Covered:

Week 7: AI-Powered Content, Communication, Collaboration & Influence

Outcomes:

Topics Covered:

Week 8: AI for Meta-Learning — Master Any New Skill with Enhanced Efficiency

Outcomes:

Topics Covered:

Week 9: Bonus Topics

Topics Covered:

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