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This course covers creating effective prompts for large language models like ChatGPT and developing AI-enabled applications using the OpenAI API.

Beau Carnes - ChatGPT and the OpenAI API.png

chatgptcloneopenai3.tar.gz

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fine_tuned_model

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OpenAI has revolutionized how developers approach natural language processing, creative content generation, and AI-driven solutions across various industries.

You will begin the course with an introduction to OpenAI and its most famous GPT-based chatbot, ChatGPT. After learning the fundamentals of prompt engineering, you’ll progress to advanced techniques for writing prompts, explore practical applications of ChatGPT, and gain hands-on experience with the OpenAI API. The course will guide you through advanced model usage, including fine-tuning and embeddings, and provide insights into troubleshooting and best practices.

By the end of this course, you will be equipped with the skills to leverage the OpenAI API for innovative applications, from building AI-driven chatbots to implementing AI solutions in real-world projects. This knowledge will enhance your ability to create cutting-edge AI applications and advance your career in the rapidly evolving field of artificial intelligence.

Mastering OpenAI API and ChatGPT for Innovative Applications

Tokens are used to adjust the model's weights.

They serve as an invoicing unit for charging API users.

Tokens represent words and characters, and are used for generating the next sequence.

They are used to create a prompt for the model.

What role do tokens play in the context of the model's operations post-training?

What role do tokens play in the context of the model’s operations post-training?

A lower temperature setting makes the output more consistent, while a higher one makes it more diverse and creative.

It directly affects the number of tokens used.

Temperature settings have no impact on the model's outputs.

How does adjusting the temperature setting influence the output of a model?

How does adjusting the temperature setting influence the output of a model?

To reduce the costs associated with using the OpenAI API.

To master the art of communicating effectively with the AI model.

To understand the underlying technology of AI.

What is the ultimate goal of understanding how to craft effective prompts and manage responses?

What is the ultimate goal of understanding how to craft effective prompts and manage responses?

The max tokens should be at least 200 to handle this request.

The `openai.Completion` should be `openai.ChatCompletion`.

What is wrong with this code?
```
def analyze_sentiment(text):
    response = client.chat.completions.create(
      model="gpt-3.5-turbo",
      prompt=f"Analyze the sentiment of the following text: \"{text}\". Is it positive, negative, or neutral?",
      max_tokens=50
    )
```

What is wrong with this code?
<pre><code>def analyze_sentiment(text):
 response = client.chat.completions.create(
 model=&quot;gpt-3.5-turbo&quot;,
 prompt=f&quot;Analyze the sentiment of the following text: \&quot;{text}\&quot;. Is it positive, negative, or neutral?&quot;,
 max_tokens=50
 )
</code></pre>


What are the three most common message roles in the Chat Completions API?

What are the three most common message roles in the Chat Completions API?

Delivering initial instructions to the model.

What is a primary role that the "system" plays in a chat completion interaction?

What is a primary role that the “system” plays in a chat completion interaction?

What is the primary distinction between multi-turn and single-turn conversations?

What is the primary distinction between multi-turn and single-turn conversations?

It allows the AI to mimic human emotions.

It enables a progressive flow of ideas through follow-up prompts.

It ensures the AI can speak multiple languages.

It empowers the AI to reject irrelevant queries.

What is the significance of the multi-turn feature in making conversations with AI seem real?

What is the significance of the multi-turn feature in making conversations with AI seem real?

By using the `openai.ChatCompletion.create()` method.

By using the `openai.Chat.create()` method.

By using the `openai.Response.create()` method.

By calling the `openai.GetResponse()` function.

How is the chatbot's response obtained from the API?

How is the chatbot’s response obtained from the API?

High `temperature` and high `max_tokens`.

Which combination of parameters would most likely yield a concise yet unpredictable response from the API?

Which combination of parameters would most likely yield a concise yet unpredictable response from the API?

Introduction to OpenAI and ChatGPT

Crafting Prompts for ChatGPT

Practical Applications of ChatGPT

Advanced ChatGPT Usage

ChatGPT Assessment

Introduction to OpenAI API and Its Components

OpenAI Models

Generating Text Completions with OpenAI API

Advanced Model Usage: Fine-Tuning Models

Exploring Embeddings with the OpenAI API

Troubleshooting, Limitations, and Best Practices with OpenAI API

Real-World Applications of OpenAI API

Wrapping Up

Quiz on Completions