Introduction to Course

Hello and welcome to Mastering Generative AI with OpenAI. My name is Janakiram MSV, and I’ll be your instructor. OpenAI is one of the most mature generative AI platforms and powers ChatGPT, the widely used conversational AI. In this lesson we’ll introduce foundation models (large models trained on broad data) and the OpenAI API. This course is designed to give you a clear, practical overview of the OpenAI platform, its models, and how to use the API to build real-world applications. What we’ll cover in this course:

Fundamentals of generative AI and how it differs from traditional machine learning and deep learning.
Overview of foundation models and the OpenAI model family (GPT, DALL·E, Whisper).
Prompt engineering and building effective context for better model outputs.
Understanding embeddings and how to use them for semantic search and retrieval.
Practical workflows for fine-tuning, chaining prompts, and integrating the API into applications.
Hands-on labs using Jupyter notebooks so you can experiment with code and concepts.

Key concepts and use cases:

Content generation (articles, summaries, code).
Conversational agents and chatbots.
Image generation with DALL·E.
Speech-to-text using Whisper.
Text classification, semantic search, and retrieval-augmented generation (RAG).

A minimal Jupyter notebook setup (used in course labs):

# Install minimal dependencies (example)
pip install openai jupyter

# Minimal notebook setup
import os
import openai

# Optionally set API key in environment: export OPENAI_API_KEY="sk-..."
openai_api_key = os.getenv("OPENAI_API_KEY")

A short context example (used in labs to illustrate prompt/context handling):

context = '''
Steve Jobs' 2005 Stanford Commencement Address

The first story is about connecting the dots.
I dropped out of Reed College after the first six months, but then stayed around as a

It started before I was born. My biological mother was a young, unwed college graduate.

And 17 years later I did go to college. But I naively chose a college that was almost

It wasn't all romantic. I didn't have a dorm room, so I slept on the floor in friends'

Reed College at that time offered perhaps the best calligraphy instruction in the country.

None of this had even a hope of any practical application in my life. But ten years

Again, you can't connect the dots looking forward; you can only connect them looking

My second story is about love and loss.

I was lucky — I found what I loved to do early in life. Woz and I started Apple in

I really didn't know what to do for a few months. I felt that I had let the previous

I didn't see it then, but it turned out that getting fired from Apple was the best

During the next five years, I started a company named NeXT, another company named Pixar.

I'm pretty sure none of this would have happened if I hadn't been fired from Apple. It was awful tasting medicine, but

My third story is about death.
'''

A screenshot of the OpenAI API documentation page showing a left-hand navigation menu and a center list of community libraries. On the right is a large portrait photo of a person wearing glasses.

Course Outline

Module	Focus	What you’ll practice
Fundamentals of Generative AI	Foundation models, differences from traditional ML	Interpreting model capabilities and limitations
Prompt Engineering & Context	Designing prompts, dynamic context, system/user roles	Hands-on prompt tuning in notebooks
GPT: Text Generation & Analysis	Content generation, summarization, code generation	Building simple chatbots and summarizers
Embeddings & Retrieval	Semantic search, vector stores, similarity search	Create small RAG pipelines with embeddings
Images & Audio	DALL·E image generation, Whisper speech-to-text	Generate images and transcribe audio
Fine-tuning & Production	Fine-tuning approaches and deployment best practices	Minimal fine-tuning demos and deployment patterns

</Course Outline>

Recommended prerequisites: basic Python, familiarity with Jupyter notebooks, and a working OpenAI API key. These will help you follow the hands-on labs and example notebooks.

KodesKloud hosts a community forum where you can post questions and answers. I invite you to join the vibrant KodeKloud community to collaborate with peers and share learning experiences. Links and references:

Important: Keep your OpenAI API keys secure. Do not hard-code keys in notebooks that you share publicly. Use environment variables or a secrets manager for production workloads.

By the end of this course you’ll be confident using the OpenAI APIs to build modern applications powered by generative AI. Fasten your seatbelts and get ready for an exciting journey into the world of generative AI.

Introduction

What is Generative AI

Getting Started with OpenAI

Exploring ChatGPT

Understanding Prompt Engineering

Understanding Tokens and API Parameters

Implementing Word Completion

Implementing Code Completion

Building an Interactive Chatbot

Performing Text Processing and Analysis

Using Word Embeddings For Dynamic Context

Fine-tuning GPT-3 with a Custom Dataset

Generating Images

Audio Transcription & Translation

Moderating Prompts with Moderating API

Summary and Next Steps

Introduction to Course

Watch Video