Build a Strong AI Foundation

• Introduces core concepts of Generative AI and Natural Language Processing (NLP) 
• Defines Generative AI and distinguishes it from traditional AI 
• Explores the historical evolution and strategic importance of Generative AI in various industries 
• Covers fundamental NLP concepts and their historical development 
• Provides a foundation for understanding how language models function and their application

• Delves into advanced prompting techniques for complex business scenarios 
• Explores Generative AI-powered applications for business intelligence across diverse sectors 
• Covers techniques to enhance LLM performance 
• Introduces Retrieval-Augmented Generation (RAG) for improved output accuracy and relevance

• Provides strategies for managing Generative AI changes within organizations 
• Covers common pitfalls and implementation steps 
• Evaluates implementation costs and user impact 
• Explores real-world use cases in healthcare, HR, and marketing

• Introduces the concept and evolution of AI agents 
• Explains how agents differ from traditional models using real-world analogies 
• Covers key concepts: environment, autonomy, memory, reasoning, and tool use 
• Identifies classical agent types and modern patterns like REACT and Reflection 
• Explores business applications of AI agents

• Focuses on the practical applications of AI agents 
• Differentiates between agentic workflows and autonomous agents 
• Applies REACT and Reflection for task-specific agent design 
• Covers designing agentic workflows and AI agents by defining roles, prompts, memory, and tool access. 
• Emphasizes selecting tools effectively and implementing memory strategies for reliable, personalized, and intelligent agent behavior. 
• Explains monitoring through observability, evaluation, and feedback loops 
• Focuses on avoiding common design and deployment pitfalls

• Addresses key Responsible AI concepts, including risks and ethics 
• Identifies major LLM security risks (jailbreaking, prompt injection, data poisoning, insecure outputs) 
• Explains how supply chain vulnerabilities and service denial affect LLM reliability and accountability. 
• Teaches the application of the CIA Triad (Confidentiality, Integrity, Availability) to assess and mitigate security risks in LLM deployments. 
• Analyzes real-world LLM failures across various sectors, focusing on competence versus hallucination. 
• Explains why LLMs struggle with reasoning and strategies like Chain-of-Thought and Retrieval-Augmented Generation to improve reasoning. 
• Promotes safe and accountable LLM usage by verifying outputs, applying ethical practices, and ensuring transparency

• Final assessment to test understanding of core concepts 
•  Includes a reflective essay to apply strategic thinking to real-world problems using Generative AI and Agents

Modules designed to help you learn at your own pace, while building strong foundational expertise. 

• Build a simple LLM workflow 
• Large Language Model Architecture and Mechanics