OPENAI_API_KEY

gpt.tar.gz

Python

Golang

Java

Sandbox

Python_textGen

In this course, you’ll start a transformative learning journey exploring the applications of GPT-3 in AI and gaining hands-on experience with this powerful language model.

You’ll start the course by understanding the basics of GPT-3 and its applications across domains and then learn to use the OpenAI API with Python, Go, and Java. Next, you’ll dive into prompting techniques with GPT-3, exploring essential topics to understand how to use GPT-3 for next-gen startups with real-world use cases and applications. You’ll explore its role in prominent companies like GitHub, Algolia, and Microsoft’s Azure. Lastly, you will navigate the ethical considerations of GPT-3, addressing issues like AI bias, anti-bias countermeasures, and the environmental impact of LLMs.

After completing this course, you’ll gain a deep understanding of GPT-3. Whether you are an aspiring developer, entrepreneur, or professional transitioning to AI-focused roles, this course equips you with the skills to advance your career.

Unleashing the Power of AI with OpenAI’s GPT-3

To proofread and correct grammar errors in existing short stories.

To assist users in generating compelling short stories by providing starting sentences or scenarios.



To convert written short stories into audio format for a podcast.

**Scenario:**
Imagine you are developing an AI-powered creative writing assistant designed to help users generate compelling short stories. The system prompts users with a starting sentence or scenario and encourages them to continue the narrative. The goal is to provide inspiration and guidance for creative writing enthusiasts.

What is the primary purpose of the AI-powered creative writing assistant in this scenario?

Scenario:
Imagine you are developing an AI-powered creative writing assistant designed to help users generate compelling short stories. The system prompts users with a starting sentence or scenario and encourages them to continue the narrative. The goal is to provide inspiration and guidance for creative writing enthusiasts.
What is the primary purpose of the AI-powered creative writing assistant in this scenario?

"Tell me about your favorite restaurants."

"What is your preferred mode of transportation?"

"Please enter your preferred cuisine type."

**Scenario:** You are developing a chatbot to assist users in finding local restaurants based on their preferences. Users can input their desired cuisine, price range, and dietary restrictions to receive personalized restaurant recommendations.

What would be an effective prompt for the chatbot to elicit information about the user’s preferred cuisine?

Scenario: You are developing a chatbot to assist users in finding local restaurants based on their preferences. Users can input their desired cuisine, price range, and dietary restrictions to receive personalized restaurant recommendations.
What would be an effective prompt for the chatbot to elicit information about the user’s preferred cuisine?

"Analyze my grocery expenses from the last two days."

"Summarize my grocery expenses for the previous year."

**Scenario:**
You are using an AI bot that assists you in managing your personal expenses and budget. The bot can provide insights, categorize expenses, and offer suggestions to optimize spending. You want to inquire about your recent grocery expenses.

To efficiently retrieve information about specific grocery items purchased last week, which prompt will likely yield the most relevant data?

Scenario:
You are using an AI bot that assists you in managing your personal expenses and budget. The bot can provide insights, categorize expenses, and offer suggestions to optimize spending. You want to inquire about your recent grocery expenses.
To efficiently retrieve information about specific grocery items purchased last week, which prompt will likely yield the most relevant data?

 "Analyze my expenses on different items."

"Break down my grocery costs by category."



"Tell me about my preferred grocery items."



**Scenario:**
You are using an AI bot that assists you in managing your personal expenses and budget. The bot can provide insights, categorize expenses, and offer suggestions to optimize spending. You want to inquire about your recent grocery expenses.

When aiming to understand the distribution of grocery expenses across various categories (e.g., fruits, vegetables, snacks), which prompt is the most effective?

Scenario:
You are using an AI bot that assists you in managing your personal expenses and budget. The bot can provide insights, categorize expenses, and offer suggestions to optimize spending. You want to inquire about your recent grocery expenses.
When aiming to understand the distribution of grocery expenses across various categories (e.g., fruits, vegetables, snacks), which prompt is the most effective?

**Scenario:**
You are developing a generative model using OpenAI’s API, which is programmed to generate creative and contextually relevant marketing copy for various products. The model aims to understand user input, retrieve relevant information, and generate engaging product descriptions. You can only use the provided endpoints: `List engines`, `Retrieve engines`, `Completions`, `Semantic search`, `Files`, and `Embeddings`.

Suppose you want to enhance your generative model’s understanding of user queries for marketing copy. What would be the next logical endpoint to implement following the initial setup?

Scenario:
You are developing a generative model using OpenAI’s API, which is programmed to generate creative and contextually relevant marketing copy for various products. The model aims to understand user input, retrieve relevant information, and generate engaging product descriptions. You can only use the provided endpoints: <code>List engines</code>, <code>Retrieve engines</code>, <code>Completions</code>, <code>Semantic search</code>, <code>Files</code>, and <code>Embeddings</code>.
Suppose you want to enhance your generative model’s understanding of user queries for marketing copy. What would be the next logical endpoint to implement following the initial setup?

**Scenario:**
You are developing a generative model using OpenAI's API, which is programmed to generate creative and contextually relevant marketing copy for a variety of products. The model aims to understand user input, retrieve relevant information, and generate engaging product descriptions. You can only use the provided endpoints: `List engines`, `Retrieve engines`, `Completions`, `Semantic search`, `Files`, and `Embeddings`.

After implementing `Semantic search`, which API endpoint would you integrate to allow the model to access external documents or databases for richer content?

Scenario:
You are developing a generative model using OpenAI’s API, which is programmed to generate creative and contextually relevant marketing copy for a variety of products. The model aims to understand user input, retrieve relevant information, and generate engaging product descriptions. You can only use the provided endpoints: <code>List engines</code>, <code>Retrieve engines</code>, <code>Completions</code>, <code>Semantic search</code>, <code>Files</code>, and <code>Embeddings</code>.
After implementing <code>Semantic search</code>, which API endpoint would you integrate to allow the model to access external documents or databases for richer content?

**Scenario:**
You are developing an image recognition system for a specialized industry that involves unique and custom objects. The system needs to quickly adapt to recognizing these specific objects with minimal labeled examples. Since the objects are rare and have distinctive features, a few-shot learning approach is employed to provide the model with a small set of examples for each custom object category. This allows the model to generalize and recognize similar objects accurately with limited training data.

What sort of operation needs to be performed on the model?

Scenario:
You are developing an image recognition system for a specialized industry that involves unique and custom objects. The system needs to quickly adapt to recognizing these specific objects with minimal labeled examples. Since the objects are rare and have distinctive features, a few-shot learning approach is employed to provide the model with a small set of examples for each custom object category. This allows the model to generalize and recognize similar objects accurately with limited training data.
What sort of operation needs to be performed on the model?

**Scenario:** 
You are working on a natural language processing application that needs to understand and categorize text in multiple languages. The challenge is that the system should be able to classify content without specific training in each language. The model is designed to leverage its understanding of semantic relationships and features to categorize text across languages without the need for language-specific training data. This allows the system to handle new languages seamlessly without retraining for each one.

What sort of operation needs to be performed on the model?

Scenario:
You are working on a natural language processing application that needs to understand and categorize text in multiple languages. The challenge is that the system should be able to classify content without specific training in each language. The model is designed to leverage its understanding of semantic relationships and features to categorize text across languages without the need for language-specific training data. This allows the system to handle new languages seamlessly without retraining for each one.
What sort of operation needs to be performed on the model?

Let’s test your knowledge of the concepts covered in this chapter.

Course Introduction

The Large Language Model Revolution

Getting Started with OpenAI API

GPT-3 and Programming

GPT-3 Enabler for Next-Gen Startups

GPT-3 as the Next Step in Corporate Innovation

GPT-3—The Good, the Bad, and the Ugly

Conclusion—Democratizing Access to AI

GPT-3: Prompt Engineering and Uses

Quiz: Getting Started with OpenAI API