ChatGPT Prompt Engineering Course for Developers

Topics

• Generative AI Principles
• Types of Generative Models
• Applications of Generative Models
• Machine Learning Algorithms with GenAI
• Applications of Generative AI
• Generative AI Advantages and Disadvantages
• Ethical Considerations

Hands-on Projects

• Implementing Generative AI Use Cases
• Implementing Machine Learning with GenAI

Skills You Will Learn

• Generative AI Fundamentals
• Generative AI for Problem Solving
• Ethical Insights into Generative AI

Topics

• Natural Language Processing (NLP) Essentials
• Text Classification
• Text Preprocessing
• Basic NLP Tasks
• Deep Learning for NLP
• Neural Networks
• Backpropagation
• RNN
• LSTM
• Deep Learning Applications in NLP

Hands-on Projects

• Simple Text Classification Task
• Implementing Core NLP Tasks
• Working with Deep Learning for NLP

Skills You Will Learn

• Text Classification
• Text Pre-processing
• Deep Learning Applications for NLP
• Neural Network Architectures

Topics

• Basic Autoencoders
• Variational Autoencoders (VAEs)
• Applications in Data Compression and Generation
• Basic GAN Architecture
• Training GANs
• Variants of GANs
• DCGAN
• CycleGAN

Hands-on Projects

• Working with GANs
• Data Compression with Autoencoders
• Training Variants of GANs

Skills You Will Learn

• Working with Autoencoders and VAEs
• GAN Architecture
• GAN Training Techniques

Topics

• Exploring Language Models
• Types of Language Models
• Applications of Language Models
• The Transformer Architecture Attention Mechanism
• Advanced Transformer Models
• GPT
• BERT
• Applications of Transformer-based Models

Hands-on Projects

• Working with GPT
• Implementing BERT
• Applying Advanced Transformer Models

Skills You Will Learn

• Working with Language Models
• Transformer Architectures
• Implementing Transformer-based Models

Topics

• Prompt Engineering Principles
• What is Prompt Engineering?
• Importance and Applications
• Prompt Design Strategies
• Types of Prompting
• Crafting Effective Prompts
• Parameter Tuning

Hands-on Projects

• Designing Precise Prompts
• Experimenting with Various Prompt Design Strategies
• Advanced Parameter Tuning for Prompt Engineering

Skills You Will Learn

• Prompt Engineering Principles
• Crafting Effective Prompts
• Advanced Parameter Tuning for Prompts

In this module, participants will be introduced to the world of Large Language Models, laying the foundation for understanding their architecture and functionalities. 

Lessons:

• Overview of Large Language Models.
• Introduction to the OpenAI API.
• Understanding the Significance of Large Language Models in AI.

Lab:

• Hands-on session: Exploring various Large Language Models.
• Interactive exercises: Working with the OpenAI API.

After completing this module, students will be able to:

• Understand the basic concepts of Large Language Models.
• Gain insights into the architecture and functionalities of various models.

This module will delve into the prompting world, offering participants a comprehensive understanding of their functionality and application. 

Lessons:

• Introduction to Guidelines for Prompting.
• Exploring the Principles of Writing Clear and Specific Instructions.
• Understanding Model Limitations: Hallucinations.

Lab:

• Hands-on workshop: Working with effective prompting.
• Interactive exercises: Identifying and mitigating model limitations.

After completing this module, students will be able to:

• Understand the concept and application of effective prompting.
• Gain hands-on experience with writing clear and specific instructions.

In this module, participants will learn the techniques for iterative prompt development and how to address various issues that may arise. 

Lessons:

• Introduction to Iterative Prompt Development.
• Handling Issues: Text Length, Focus Details, and Table of Dimensions.

Lab:

• Hands-on workshop: Developing and refining prompts iteratively.
• Interactive exercises: Addressing issues in prompt development.

After completing this module, students will be able to:

• Understand and apply iterative prompt development techniques.
• Efficiently handle and resolve issues in prompt development.

This module will explore various summarizing techniques, focusing on different aspects such as shipping, delivery, price, and value. 

Lessons:

• Techniques for Effective Text Summarization.
• Summarization with a Focus on Specific Aspects.

Lab:

• Hands-on session: Practicing summarization techniques.
• Interactive exercises: Focusing summarization on specific aspects.

After completing this module, students will be able to:

• Master various text summarization techniques.
• Effectively summarize text with a focus on specific aspects.

Participants will delve into the techniques for inferring topics and creating news alerts for certain topics in this module. 

Lessons:

• Introduction to Topic Inferring.
• Creating News Alerts for Specific Topics.

Lab:

• Hands-on workshop: Working with topic inferring.
• Interactive exercises: Creating and customizing news alerts.

After completing this module, students will be able to:

• Understand and apply topic inferring techniques.
• Create customized news alerts for specific topics.

This module will cover various text transformation techniques including translation, tone transformation, and format conversion. 

Lessons:

• Exploring Various Text Transformation Techniques.
• Deep Dive into Translation, Tone Transformation, and Format Conversion.

Lab:

• Hands-on session: Practicing text transformation techniques.
• Interactive exercises: Working with translation, tone transformation, and format conversion.

After completing this module, students will be able to:

• Master various text transformation techniques.
• Efficiently translate, transform tone, and convert formats of texts.

In this module, participants will learn techniques for effectively expanding text and customizing automated replies to customer emails. 

Lessons:

• Techniques for Effective Text Expansion.
• Customizing Automated Replies to Customer Emails.

Lab:

• Hands-on session: Practicing text expansion techniques.
• Interactive exercises: Customizing automated email replies.

After completing this module, students will be able to:

• Master various text expansion techniques.
• Customize automated replies effectively.

In this module, participants will delve into the world of OpenAI embeddings, understanding their significance and application in various tasks. 

Lessons:

• Introduction to OpenAI Embeddings.
• Understanding the Applications and Use Cases of OpenAI Embeddings.

Lab:

• Hands-on session: Working with OpenAI Embeddings.
• Interactive exercises: Experimenting with various embedding techniques.

After completing this module, students will be able to:

• Understand the concept and application of OpenAI Embeddings.
• Gain hands-on experience with OpenAI Embeddings.

This module will introduce participants to function calling in ChatGPT, enhancing their understanding and skills. 

Lessons:

• Overview of Function Calling in ChatGPT.
• Practical Applications of Function Calling.

Lab:

• Hands-on session: Exploring function calling in ChatGPT.
• Interactive exercises: Implementing function calling in tasks.

After completing this module, students will be able to:

• Understand the concept of function calling in ChatGPT.
• Apply function calling effectively in various tasks.

Participants will explore custom instructions in ChatGPT in this module, gaining insights into their utilization for enhanced task performance. 

Lessons:

• Introduction to Custom Instructions in ChatGPT.
• Utilizing Custom Instructions for Task Enhancement.

Lab:

• Hands-on workshop: Working with custom instructions in ChatGPT.
• Interactive exercises: Enhancing tasks using custom instructions.

After completing this module, students will be able to:

• Understand the use of custom instructions in ChatGPT.
• Effectively utilize custom instructions for various tasks.

In this module, participants will delve into the world of fine-tuning in ChatGPT, enhancing their models for optimal performance. 

Lessons:

• Overview of Fine-Tuning in ChatGPT.
• Techniques for Effective Fine-Tuning.

Lab:

• Hands-on session: Practicing fine-tuning techniques in ChatGPT.
• Interactive exercises: Enhancing model performance through fine-tuning.

After completing this module, students will be able to:

• Understand the concept of fine-tuning in ChatGPT.
• Apply effective fine-tuning techniques for optimal model performance.

In this module, participants will delve into the integration of ChatGPT using JavaScript. This module will enhance their skills in integrating ChatGPT into web projects and applications, ensuring seamless interaction and functionality. 

Lessons:

•  Introduction to ChatGPT Integration in JavaScript.
• Working with ChatGPT: Sending Requests and Handling Responses in JavaScript.
• Best Practices for ChatGPT Integration in JavaScript.

Lab:

• Hands-on session: Setting up and integrating ChatGPT in a JavaScript environment.
• Interactive exercises: Crafting and sending requests to ChatGPT and handling the responses effectively in JavaScript.

After completing this module, students will be able to:

• Successfully integrate ChatGPT into web projects using JavaScript.
• Understand the intricacies of working with ChatGPT in a JavaScript environment.
• Apply best practices for efficient and effective ChatGPT integration in JavaScript projects.

Participants will explore the OpenAI Playground in this module to understand its features and benefits. 

Lessons:

• Overview of the OpenAI Playground.
• Benefits and Features of Using the OpenAI Playground.

Lab:

• Hands-on session: Navigating the OpenAI Playground.
• Interactive exercises: Working with features of the OpenAI Playground.

After completing this module, students will be able to:

• Understand the OpenAI Playground and its functionalities.
• Effectively use the OpenAI Playground for various tasks.

This module will focus on the critical aspect of data privacy in working with ChatGPT and other language models. 

Lessons:

• Overview of Data Privacy Concerns in Language Models.
• Best Practices for Ensuring Data Privacy.

Lab:

• Interactive session: Discussing real-world scenarios and solutions for data privacy.
• Hands-on exercises: Implementing data privacy best practices.

After completing this module, students will be able to:

• Understand the importance of data privacy.
• Implement best practices for ensuring data privacy in their projects.

In this module, participants will explore the process of creating a chatbot, focusing on the ChatGPT platform. 

Lessons:

• Overview of Chatbot Creation.
• Steps and Best Practices in Creating a Chatbot with ChatGPT.

Lab:

• Hands-on session: Building a basic chatbot using ChatGPT.
• Interactive exercises: Experimenting with chatbot features and functionalities.

After completing this module, students will be able to:

• Understand the process of chatbot creation.
• Build a basic chatbot using ChatGPT.

This module will delve deeper into the chatbot creation, focusing on creating a specific type of chatbot - OrderBot.

Lessons:

• Understanding Advanced Features of Chatbots.
• Detailed Steps in Creating OrderBot.

Lab:

• Hands-on workshop: Creating OrderBot with advanced features.
• Interactive exercises: Working with OrderBot and understanding its advanced functionalities.

After completing this module, students will be able to:

• Understand advanced features of chatbots.
• Create and work with an advanced OrderBot.

This concluding module will summarize the key learnings from the course and discuss potential future explorations in the field of ChatGPT and prompt engineering. 

Lessons:

• Summary of Key Learnings from the Course.
• Discussion on Future Explorations in ChatGPT and Prompt Engineering.

Lab:

• Interactive session: Review and reflection on the course learnings.
• Group discussions: Sharing insights and future aspirations in the field.

After completing this module, students will be able to:

• Summarize the key learnings from the course.
• Discuss potential future explorations in ChatGPT and prompt engineering.