Generative AI (Gen AI)

Overview

This course introduces learners to the fundamentals of Generative Artificial Intelligence (GenAI) and its practical applications. It begins with foundational concepts like AI, machine learning, and generative models before diving into specialized areas such as large language models, prompt engineering, and text/image generation. Learners will explore retrieval-augmented generation (RAG), AI APIs, and agent-based systems. The curriculum culminates in building a full GenAI application and completing a mini-project focused on resume generation.

Objectives

• Understand the core concepts of Generative AI, including LLMs, transformers, and tokenization.
• Develop skills in prompt engineering and apply various prompting strategies for effective model interaction.
• Implement text and image generation tasks using foundational models and tools.
• Design and build end-to-end Generative AI applications with RAG, APIs, and agent-based workflows.

Key topics

• Introduction to Generative AI and its lifecycle
• Large Language Models and their architecture
• Prompt Engineering and best practices
• Text and image generation techniques
• Retrieval-Augmented Generation (RAG) workflows
• Building AI agents and integrating APIs

Expected outcomes

• Create and optimize prompts for different generative AI tasks such as summarization and translation.
• Build and deploy a functional Generative AI application using RAG and API integration.
• Evaluate and mitigate risks related to bias, hallucinations, and ethical considerations in AI systems.

How the AI Tutor can help

How the Skillama AI Tutor helps with the listed lectures: explanations, follow-up questions, and running or debugging practice code.

Who this is for

This course is for professionals and learners seeking to understand and apply Generative AI technologies in real-world scenarios.

Curriculum

1. 1. Introduction to Generative AI

   • What is Artificial Intelligence?
   • Machine Learning vs Deep Learning
   • What is Generative AI?
   • Applications of Generative AI
   • Generative AI Lifecycle
   • Popular GenAI Models
   • Set up Python Environment
   • Access OpenAI/Gemini Playground
   • Explore AI Chat Interfaces

2. 2. Large Language Models (LLMs)

   • Introduction to LLMs
   • Transformer Architecture
   • Tokens and Embeddings
   • Context Window
   • Experiment with Temperature
   • Analyze Context Length

3. 3. Prompt Engineering

   • Prompt Engineering Basics
   • Zero-shot Prompting
   • One-shot Prompting
   • Few-shot Prompting
   • Chain of Thought Prompting
   • Prompt Best Practices
   • Create Reusable Prompt Templates

4. 4. Text Generation

   • Content Generation
   • Summarize Documents
   • Translation and Paraphrasing
   • Question Answering
   • Text Classification
   • Perform Sentiment Analysis

5. 5. Image Generation

   • Text-to-Image Models
   • Diffusion Models
   • Image Prompting and Editing
   • Style Transfer
   • Image Safety
   • Create Marketing Posters

6. 6. Retrieval-Augmented Generation (RAG)

   • Introduction to RAG
   • Vector Databases
   • Embeddings
   • Semantic Search
   • Knowledge Bases
   • RAG Workflow
   • • Build Simple RAG Pipeline

7. 7. AI APIs

   • API Fundamentals
   • REST APIs
   • Authentication
   • Model Parameters
   • Rate Limits
   • API Best Practices
   • Generate API Keys
   • Call AI APIs
   • Handle API Responses

8. 8. AI Agents

   • Introduction to AI Agents
   • Agent Components
   • Planning and Memory
   • Tool Usage
   • Multi-Agent Systems
   • Agent Use Cases
   • Create Multi-step Workflow
   • Test Agent Behavior

9. 9. Fine-tuning and Customization

   • Fine-tuning Concepts
   • Instruction Tuning
   • LoRA Basics
   • Model Evaluation
   • Dataset Preparation
   • Fine-tune Small Model
   • Evaluate Responses
   • Compare Base vs Fine-tuned

10. 10. Responsible AI

    • AI Ethics
    • Bias and Fairness
    • Hallucinations
    • Privacy and Security
    • Responsible AI Guidelines
    • Governance
    • Identify Biased Outputs
    • Evaluate Hallucinations
    • Document AI Risks

11. 11. Generative AI Applications

    • Chatbots
    • Code Generation
    • Document Automation
    • AI Assistants
    • Business Use Cases
    • Industry Applications
    • Build FAQ Chatbot
    • Automate Email Drafts
    • Create Document Summaries

12. 12. Capstone Project

    • Project Planning
    • Architecture Design
    • Deployment Concepts
    • Build End-to-End GenAI Application
    • Implement Prompt Engineering
    • Integrate AI API
    • Present Final Project

13. 13. Mini Project

    • AI Resume Generator
    • User Details Input
    • AI Resume Generation
    • Skills & Summary Generation
    • Resume Customization
    • Download Resume
    • File Storage
    • Report Generation