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Generative AI & Prompt Engineering

   

This course provides an in-depth introduction to generative AI and the principles of prompt engineering. Students will learn about various generative models, how they work, and how to craft effective prompts to interact with these models. The course combines theoretical knowledge with practical, hands-on experience.

Course Content
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Week 1: Introduction to Generative AI

  • What is Generative AI?

    • Definition and Scope

    • Historical Development of Generative AI

    • Applications of Generative AI in Different Domains

  • Overview of Popular Generative Models

    • GPT (Generative Pre-trained Transformer)

    • Variational Autoencoders (VAEs)

    • Generative Adversarial Networks (GANs)

  • Practical Session: Setting Up Environment and Tools

    • Installation and Configuration of AI Frameworks (e.g., TensorFlow, PyTorch)

    • Introduction to AI Development Platforms (e.g., OpenAI, Hugging Face)

Week 2: Understanding Transformers and Language Models

  • Basics of Transformer Architecture

    • Attention Mechanism

    • Encoder-Decoder Architecture

  • How Language Models Work

    • Pre-training and Fine-tuning

    • Understanding Tokenization and Embeddings

  • Practical Session: Experimenting with Pre-trained Models

    • Using GPT and BERT Models

    • Fine-tuning a Pre-trained Language Model

Week 3: Introduction to Prompt Engineering

  • What is Prompt Engineering?

    • Definition and Importance

    • Role of Prompts in Generative AI

  • Basic Techniques for Crafting Prompts

    • Understanding Context and Intent

    • Using Keywords and Phrases

  • Practical Session: Crafting and Testing Simple Prompts

    • Generating Text Based on Basic Prompts

    • Analyzing Model Outputs

Week 4: Advanced Prompt Engineering Techniques

  • Advanced Prompting Strategies

    • Controlling Output Length and Style

    • Managing Temperature and Diversity Parameters

  • Prompting for Different AI Tasks

    • Text Completion

    • Question Answering

    • Text Classification

  • Practical Session: Developing Complex Prompts

    • Creating Prompts for Specific Use Cases

    • Fine-tuning Prompts for Desired Outcomes

Staffing Support​

  • Resume Preparation

  • Mock Interview Preparation

  • Phone Interview Preparation

  • Face to Face Interview Preparation

  • Project/Technology Preparation

  • Internship with internal project work

  • Externship with client project work

Week 5: Evaluating and Refining Prompts

  • Evaluating AI Outputs

    • Metrics for Quality Assessment

    • Identifying Biases and Errors

  • Iterative Prompt Refinement

    • Techniques for Improving Prompt Effectiveness

    • Understanding Model Feedback Loops

  • Practical Session: Iterative Prompt Development

    • Refining Prompts Based on Feedback

    • Enhancing Prompt Performance for Various Tasks

Week 6: Applications and Ethical Considerations

  • Real-world Applications of Prompt Engineering

    • Content Creation and Creative Writing

    • AI-assisted Decision Making

  • Ethical Considerations in Generative AI

    • Addressing Bias and Fairness

    • Ethical Use of Generative AI Tools

  • Practical Session: Capstone Project

    • Designing and Implementing a Prompt Engineering Project

    • Presenting Project Outcomes and Ethical Analysis

 

Final Assessment:

  • Project Submission: Students will submit their final projects, showcasing their understanding of generative AI and prompt engineering.

  • Oral Presentation: Students will present their projects and answer questions regarding their approach, challenges, and ethical considerations.

Our Salient Features:

  • Hands-on Labs and Homework

  • Group discussion and Case Study

  • Course Project work

  • Regular Quiz / Exam

  • Regular support beyond the classroom

  • Students can re-take the class at no cost

  • Dedicated conf. rooms for group project work

  • Live streaming for the remote students

  • Video recording capability to catch up the missed class

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