Generative AI
Program

The first week introduces the AI landscape, emphasizing Generative AI fundamentals and practical business applications across data types. We'll cover key AI and Machine Learning concepts, and you'll gain a clear understanding of what this program will focus on.

• AI and Generative AI
• Generative AI - Business Landscape and Overview
• Generative AI - Tools & Level of Exploration in the Program  

Key Skills

• The Conceptual Connection between AI and Generative AI
• Large Language Models and the Generative AI Timeline

This week focuses on Prompt Engineering, a key element for maximizing Generative AI models' potential. We'll cover aligning AI outputs with business needs without coding, delve into vital themes and techniques for crafting effective prompts, and explore real-world business challenges where Prompt Engineering is essential for innovative solutions.

• Prompt Engineering for Large Language Models
• Themes and Techniques for Crafting Effective Prompts
• Business Use-Cases for Prompt Engineering

Key Skills

• Elements of Effective Prompt Design
• Few-shot Prompting and Chain-of-Thought Prompting

Case Studies: Patient Review Analysis, Customer Return Processing, Software Feedback Analysis

This project aims to create a prompt template that allows a Generative AI solution to perform Sentiment Analysis on product reviews. The model should extract the relevant information, such as product names, reviewer names, review ratings, review descriptions, and sentiment (positive or negative), to assist the company in understanding and responding to customer feedback better and faster.

In this learning week, we'll delve into the concepts behind Large Language Models (LLMs) in Generative AI, which have revolutionized Conversational Agents, serving as versatile AI Assistants. The focus here is two-fold: understanding the framework behind these Conversational Agents and exploring techniques to enhance their capabilities. This includes improving skills like memory, reasoning, and accessing private data, making AI assistants more effective for personalized tasks.

• Conversational AI - A fine-tuned version of Large Language Models
• The Framework for Generative Conversational AI Assistants

Key Skills

• Utilizing Generative Conversational Assistants
• Augmenting the capabilities of Conversational Assistants

Case Studies: Automated email-based customer support, Virtual Banking Assistant 

This week, you'll explore the world of Speech and Audio data, often converted to text for AI use. We'll cover the multi-stage pipeline needed for converting Speech to Text using Generative AI. You'll also have the chance to create a Proof of Concept for an Audio Transcription Assistant, which can be used for tasks like turning meeting recordings into accurate text transcriptions and concise summaries for efficient information access. This hands-on experience will help you apply Generative AI to real-world speech processing and text summarization tasks.

• Generative AI for Speech-to-Text Conversion
• Audio Transcription Assistant Proof of Concept

Key Skills

• The Multi-stage Speech-to-Text Conversion Pipeline
• Generative Text Transcription from Speech and Audio

Case Studies: Speech emotion recognition and response

This week introduces image data, an unstructured data form crucial in the age of Generative AI. You'll learn the framework for dual comprehension of concepts through text and images, a key feature in modern prompt-based Image Generation systems. This knowledge will enable you to use Generative AI to create high-quality images based on text prompts. Crafting effective prompts that align with business needs will open up new possibilities for creative and visual content generation.

• Image-Text Mappings in Latent Space
• Generative AI for Text-to-Image Conversion

Key Skills

• Generative Diffusion Models for Image Creation
• Prompt Engineering Strategies for Generative Image Models

Case Studies: Visual Brand Creation, Marketing Creatives Generation, Visual Storytelling with AI

This project aims to use a Generative AI solution empowered by Retrieval-Augmented Generation as a conversational assistant that is able to consume organizational documents and data and use this information to provide contextual responses to the user’s queries. This will help create a more customized solution for Generative AI that business professionals can utilize to extract utility and retrieve information from organizational resources faster, more conversationally than otherwise.

This week is a hands-on journey to acquire essential Python skills for building code-based Generative AI solutions. You'll develop proficiency in writing Python scripts, using APIs, and creating lightweight solutions for Generative AI tasks. This practical experience will teach you the fundamentals of Python and how to apply programmatic thinking to real-world business scenarios, enabling you to create custom solutions confidently.

• Fundamentals of Python Programming
• Key Data Structures and Elements for Effective Python Scripts

Key Skills

• Variables, Operators, Data Structures, Conditionals, Loops, 
• Functions - User Defined Functions, Calling APIs 
• String Manipulation and Formatting

In this week, you'll apply your Python programming skills to scale Generative AI for real-world business challenges. You'll learn to use API keys effectively for large-scale applications, understand key packages for scaling Prompt Engineering, implement prompting techniques in code, and evaluate your solutions using metrics. By the end of this intensive week, you'll be well-prepared to harness the full potential of Generative AI for impactful business solutions at scale.

• Python Scripts for Custom Generative AI Solutions
• Prompt Engineering at Scale through Code

Key Skills

• Accessing Large Language Models through APIs
• Code-based Prompt Engineering in Python
• Performance Evaluation of Large Language Models

This week focuses on using Generative AI for Programming Code Generation. You'll learn to effectively harness Large Language Models (LLMs) for code generation by understanding that code is like a lower-level version of natural language. You'll learn to fine-tune Natural Language LLMs on programming code data and the Prompt Engineering techniques needed to produce business-productive code-related outputs.

• Programming Code as an extension of the Natural Language modality
• Framework of Fine-tuned Large Language Models for Code Generation

Key Skills

• Utilizing Large Language Models for Code Generation
• Prompt Engineering Techniques to optimize code outputs

Case Studies: SQL & Python Code Generator, Code Optimizer, Code Explainer