Course Overview

Artificial Intelligence & Generative AI:

Module 1: The Foundations of AI and Large Language Models (LLMs)

This introductory module establishes the essential theoretical framework, distinguishing between Traditional (Discriminative) AI and the new paradigm of Generative AI. We will delve into the core concepts, including the Transformer architecture and the process by which Large Language Models (LLMs) are trained via pre-training, fine-tuning, and Reinforcement Learning from Human Feedback (RLHF). Key technical aspects such as tokenization and the context window will be clarified, alongside an essential discussion of the inherent limitations and ethical risks, such as hallucination and bias, necessitating a responsible approach to deployment.

Module 2: Advanced Prompt Engineering and Interaction

Moving beyond basic command-response, this module focuses on mastering Prompt Engineering, the critical skill for eliciting reliable and high-quality outputs from LLMs. Learners will be introduced to structured methodologies for crafting effective prompts, emphasizing the assignment of persona/role, clear definition of constraints, and specifying precise output formats. Advanced techniques will cover Few-Shot Learning (providing examples) and Chain-of-Thought (CoT) prompting, which instructs the model to articulate its reasoning before delivering the final result. Practical exercises will emphasize the iterative process required to refine and “debug” prompts for maximum operational reliability.

Module 3: Deploying Industry-Leading Generative AI Platforms

This module provides practical competency across the three major industry platforms: ChatGPT (OpenAI), Gemini (Google), and Copilot (Microsoft). The focus will be on leveraging the unique strengths of each tool for professional application. We will explore multimodal capabilities in platforms like Gemini and analyze the specialized role of Copilot in the software engineering lifecycle, including efficient code generation, debugging, and integration within the Integrated Development Environment (IDE). The module concludes with a comparative analysis of output quality, speed, and suitability for specific enterprise use cases.

Module 4: Autonomous AI Agents and Engineering Automation

Elevating the scope from simple prompting to complex systems, this module introduces the architecture and function of AI Agents. An Agent is defined as an autonomous system comprising an LLM (the intelligent core), Memory, and access to Tools (e.g., search engines, code execution). We will examine the operational flow, known as the Observe-Plan-Act Loop, which enables agents to manage multi-step, goal-oriented tasks. The module highlights the application of these agents in Engineering Automation, covering areas such as automated software testing, documentation generation, and complex workflow management using foundational concepts from agentic frameworks.

Module 5: Hands-on Gen-AI Projects and Ethical Deployment

The final module serves as the capstone, focusing on the practical implementation of Gen AI within a professional engineering context. The central topic will be Retrieval Augmented Generation (RAG), the industry standard pattern for securely grounding LLMs with proprietary enterprise data. Learners will understand the complete RAG pipeline: data ingestion, chunking, embedding, vector database search, and answer synthesis. This knowledge facilitates the design of safe, non-hallucinatory applications. The module concludes with a review of Responsible AI principles, ensuring all generated projects adhere to standards of fairness, accountability, and transparency.