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E SKILLIFY and Eng.Mariam Omar

In this course, you will discover the power of GPT-3 in creating conversational AI solutions. We will start with an introduction to chatbots and their use cases, and then dive deep into GPT-3 and its capabilities. You will learn how to fine-tune the model for specific tasks, such as customer service, lead generation, or entertainment. We will cover techniques for improving the accuracy and fluency of the chatbot's responses, as well as strategies for handling user input and managing conversation flow.

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In this course, you will discover the power of GPT-3 in creating conversational AI solutions. We will start with an introduction to chatbots and their use cases, and then dive deep into GPT-3 and its capabilities. You will learn how to fine-tune the model for specific tasks, such as customer service, lead generation, or entertainment. We will cover techniques for improving the accuracy and fluency of the chatbot's responses, as well as strategies for handling user input and managing conversation flow.

Next, we will explore different ways to integrate GPT-3 chatbots with various platforms and channels, such as messaging apps, voice assistants, and social media. You will learn how to use APIs and SDKs to connect your chatbot to these platforms and leverage their features, such as natural language processing, voice recognition, or rich media support. We will also cover best practices for designing chatbot user interfaces and testing and deploying your chatbot in production.

By the end of this course, you will have a solid understanding of how GPT-3 works and how to use it to build powerful and engaging chatbots for your business or personal projects. You will have hands-on experience with fine-tuning GPT-3 models and integrating them with various platforms and channels, and you will be ready to apply these skills in real-world scenarios.

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What's inside

Learning objectives

  • Understand key concepts of fine-tuning pre-trained models.
  • Learn to determine if fine-tuning is appropriate for a specific problem.
  • Identify types of pre-trained models and their strengths and weaknesses.
  • Prepare data for fine-tuning with tokenization and encoding.
  • Fine-tune a pre-trained model for a specific nlp application

Syllabus

Introduction to Fine-Tuning

In this lesson, we will explore the concept of GPT models, their capabilities, and limitations. We will discuss the natural language processing subfield of artificial intelligence and how GPT models use pre-training to handle human language. We will also examine the drawbacks of using pre-trained models, including the upper bound on quality, maximum prompt size, and cost and latency issues. Additionally, we will introduce the technique of fine-tuning, which allows us to adapt pre-trained models to perform specific tasks in our domains. By the end of the lesson, students will have a comprehensive understanding of GPT models and the process of fine-tuning to customize them for specific applications.

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In this lesson, you will learn about the powerful technique of fine-tuning pre-trained models for specific natural language processing (NLP) applications. You will cover key concepts and the necessary steps required to successfully fine-tune pre-trained models. Before deciding to fine-tune a model, you need to understand the problem you are working on, the task at hand, and the requirements of the task, such as speed and accuracy. You also need to know the characteristics of the dataset you are using, such as its size and type. Once you have determined that fine-tuning is the solution to your problem, you need to choose a pre-trained model that is trained on data that is similar to the data you are using. You will then prepare the data, including changing its format to a format that the pre-trained model can understand, such as tokenization and encoding. Once your data is ready, you can start fine-tuning the pre-trained model on your dataset. After fine-tuning, you will evaluate the performance of your model, try it with inputs, and analyze its outputs to improve its performance. By the end of the lesson, you will have the knowledge and skills to master the fine-tuning technique for NLP applications.


In this lesson, students will be introduced to OpenAI's Playground, an online interface that allows users to experiment and test API capabilities of chat models. The lesson will cover the various models available in the playground, how to create and use API keys to fine-tune models, and the different settings and parameters that can be used to control the model's responses. The lesson will also provide a demonstration of how to use the input and output box, as well as how to adjust parameters such as temperature and maximum length to control the model's output. By the end of this lesson, students will have a basic understanding of how to use OpenAI Playground to test and experiment with chat models.

This quiz will test your knowledge of GPT models and fine-tuning techniques. You will be asked questions about the limitations of GPT models, the steps involved in fine-tuning a model, and the benefits of fine-tuning. You will also be tested on your understanding of OpenAI's Playground and its features.

Formatting data for Fine-Tuning

In this lesson, we will explore the importance of preparing and formatting data before using it to fine-tune a model. We will discuss how properly formatted data can improve model accuracy and efficiency, reduce errors, and prevent biased information from being learned. Additionally, we will cover various types of data formats that are commonly used in machine learning and natural language processing projects and provide tips on how to prepare them for use with pre-trained models. By the end of the lesson, learners will have a better understanding of the benefits of data preparation and formatting, as well as practical techniques for optimizing data sets for use in model training.

In this lesson, we will discuss the format of the data used for fine-tuning a GPT model. We will begin by exploring how to prepare different data sets, discussing their strengths and weaknesses, and sharing tips on how to have good data that is ready for fine-tuning. We will also cover the importance of adding suffixes to data. We will go through a set of data sets, including one about Arduino and another about earthquakes, and examine them to see how to structure them correctly. We will explain why it's essential to have a single question with a related answer, feed the model with different question structures, and have at least 200 different prompts and completions. By the end of the lesson, students will have a clear understanding of how to structure data sets for fine-tuning GPT models.

In this lesson, we will learn how to use Python to clean a CSV file by removing any missing values or duplicates. Data cleaning is an important step in data preparation for fine-tuning, as duplicates and missing values can negatively impact the results of our analysis. We will be using Google Colab, an online tool that allows us to easily execute our Python code. We will also learn how to mount our Google Drive to access our data set and save our cleaned data into a new file. By the end of this lesson, you will have a solid understanding of how to clean and prepare data for further analysis using Python.

In this lesson, you will learn how to prepare questions and answers from a text file to fine-tune an AI model. The lesson will cover the two stages of formatting the data as prompts and completions with suffixes included and changing the data to JSON format. You will be introduced to an online tool, trainmy.ai/qa-generator, which can be used to generate questions and answers from a text file. The lesson will also cover how to enter text into the tool and how to verify an API key. By the end of the lesson, you will be able to generate questions and answers from a text file for fine-tuning AI models.

Generate Questions and Answers from a raw text

In this lesson, you will learn how to use ChatGPT to generate Python code for working with large datasets. You will be shown how to use prompt engineering to describe the features and requirements of your project and data set in order to get an optimal Python code. The code generated will be used to convert a prepared data set into the shape of prompts and completions. You will learn how to import necessary libraries, load data from a file, and manipulate data using prompts and completions. By the end of this lesson, you will have a solid understanding of how to use ChatGPT to generate Python code for data manipulation.

In this lesson, we will learn how to run and execute Python code on Google Colaboratory, a cloud-based platform for running Jupyter notebooks. The lesson will start by reviewing the Python code that was prepared in the previous lesson for preparing prompts and completions from a large CSV data file. We will discuss the changes that need to be made to the code to meet the requirements of our dataset.

Then, we will move on to executing the Python code step-by-step on Google Colaboratory. We will use Google Colaboratory to access and read CSV files from our Google Drive. We will also discuss how to create a new notebook and add codes or text to it before running and executing the code.

By the end of this lesson, you will have a good understanding of how to use Google Colaboratory to run and execute Python code for data preparation and analysis.

Creating prompts and completions from CSV data.

In this lesson, you will learn how to build a well-structured project directory and manage data within it. You will understand the importance of organizing your project directory, particularly in the context of data science or machine learning projects. You will learn the benefits of a good project structure, such as saving time, removing misconceptions, and avoiding overlapping work. The lesson will cover tips and best practices for creating a good project directory, including organizing data into folders and subfolders with descriptive names, keeping a README file, and including different folders for code, scripts, and data analysis. By the end of the lesson, you will have a clear understanding of how to create a project directory that is easy to navigate, understand, and reuse for future projects.

Fine-Tuning a Model

In this lesson, you will learn about the different pre-trained models offered by OpenAI's GPT-3, and how to choose the best one for your fine-tuning project. You will be introduced to the four basic models available for fine-tuning, including DaVinci, Curie, Anna, and Babbage. The lesson will cover the specifications of each model, such as how many tokens they can take and their capabilities. You will also be shown how to access the documentation section of OpenAI's website and preview the models. By the end of the lesson, you will have a clear understanding of which model is best suited for your specific fine-tuning needs.

This lesson provides an overview of the steps involved in the fine tuning process in Python, from beginning to end. The lesson covers how to download the latest version of Python, open the command prompt, locate the file, download necessary libraries and packages such as Open AI, pandas, and others. Participants will learn how to run Python codes and execute commands to fine tune their data. By the end of the lesson, participants will have a foundational understanding of the fine tuning process and will be equipped to apply these skills to their own data.

In this lesson, we will learn about the three main stages involved in fine-tuning a pre-trained model. The first stage is to change the format of the data to the adjacent format required by pre-training models, using a Python code. The second stage is to give this data to a pre-trained model to do the fine-tuning. Finally, in the third stage, we will test our fine-tuned model. We will use CMD and Python to execute these tasks. Specifically, we will download Python, open our file, and execute two Python codes included in the file to change the CSV data file to a JSON data format and communicate with OpenAI's API keys to do the fine-tuning. By the end of this lesson, you will be familiar with the process of fine-tuning pre-trained models and how to implement it in Python.

In this lesson, you will learn how to test your fine-tuned model on OpenAI Playground. The lesson will cover the basics of choosing the model you want to work with, changing the temperature to ensure the model is not using extra information, and setting the maximum token length. The lesson will also show you how to write prompts and use the stop sequence that you added to your data before fine-tuning the model. By the end of this lesson, you will be able to test your model and get responses from it using OpenAI Playground.

Develop your own chatbot based on GPT 3.5 Model

In this lesson, we will dive into the basics of using Postman as a tool to test APIs from OpenAI. We will explore how to configure the Postman environment, create workspaces and collections, and integrate the OpenAI API into our project. Starting with signing in to Postman, we will create a new workspace and collection, and then proceed to the OpenAI API playground to generate an API key. We will learn how to store the API key as a variable in Postman, set up authorization using the key, and configure the environment for seamless integration with OpenAI tools. Join us to discover the initial steps to leverage Postman and OpenAI for your projects. See you in the next lesson!

In this lesson, we will continue our journey of building a chatbot using OpenAI APIs. After setting up our workspace and collection in Postman and adding the API key to the environment, we will start by fetching data from the OpenAI documentation. Navigating to the models section, we will explore the various models provided by OpenAI, with a focus on GPT-3.5 Turbo, the model we will utilize for our chatbot. We will extract the endpoint for GPT-3.5 Turbo and return to Postman to construct our API request by specifying the endpoint.

However, during our initial request, we will encounter an authorization error, reminding us to include the API key in the authorization section. We'll rectify this by adding the key to the authorization headers. Once the authorization is set, we'll encounter another error, indicating the requirement for model parameters. It's now time to start building our chatbot.

In this tutorial, we are advancing our chatbot development journey by creating a basic web page that incorporates the OpenAI API. Having already confirmed the API's functionality using Postman, we will now move on to assembling a web page powered by ChatGPT. To achieve this, we'll furnish ChatGPT with essential instructions and parameters, like the HTML code and JSON configuration, which encompasses the endpoint URL and API key.

students will engage in data preparation, fine-tuning of a GPT-3.5 Turbo model using two different methodologies, and evaluate the performance of the fine-tuned model.

This lesson provides an overview of the updated methods for fine-tuning GPT-3.5 Turbo models, including data preparation tailored to the model's roles and responses, and introduces two approaches to the fine-tuning process.


This lesson walks through the steps of preparing data for fine-tuning a GPT-3.5 Turbo model, including formatting data in CSV and converting it to JSON using Python.

This lesson guides you through the process of fine-tuning a GPT-3.5 Turbo model using Python code. It includes steps for setting up your environment, running the fine-tuning process with your prepared dataset, and testing the fine-tuned model. The lesson covers installing necessary libraries, importing your dataset, initiating the fine-tuning job, and interpreting the results. It also provides a practical example of how to test the newly fine-tuned model with a custom prompt.


This lesson covers the final steps in the fine-tuning process of a GPT-3.5 Turbo model, including how to receive and interpret completion notifications from OpenAI. It demonstrates how to verify the fine-tuning through OpenAI's email confirmation and test the fine-tuned model directly in the OpenAI Playground.

This lesson introduces a simplified approach to fine-tuning GPT-3.5 Turbo models without extensive coding.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops advanced techniques for fine-tuning pre-trained models, which is standard in industry AI development
Focuses on fine-tuning GPT-3, specifically, which is a widely used and powerful language model
Provides hands-on experience with fine-tuning GPT-3 models and integrating them with various platforms and channels, which is highly relevant to industry AI development
Covers key concepts and the necessary steps required to successfully fine-tune pre-trained models, which is standard in industry AI development
Taught by Eng. Mariam Omar, who is an expert in natural language processing and AI
Requires students to have a basic understanding of NLP and Python, which may be a barrier to entry for some learners

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in OpenAI & ChatGPT API's: Expert Fine-tuning for Developers with these activities:
Collect relevant resources
Gather course materials such as notes, assignments, quizzes, and exams for future reference and review.
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  • Identify and gather key course materials
  • Sort and organize materials by topic or module
  • Review and highlight important concepts and information
Develop a chatbot using GPT-3
Apply knowledge of fine-tuning GPT-3 by creating a functional chatbot for a specific purpose.
Browse courses on Chatbot Development
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  • Define the purpose and scope of the chatbot
  • Design the chatbot's user interface and conversation flow
  • Fine-tune GPT-3 and integrate it into the chatbot
  • Test and evaluate the chatbot's performance
  • Deploy and maintain the chatbot
Participate in a hackathon or competition related to GPT-3
Challenge oneself by participating in hackathons or competitions to showcase skills and knowledge in using GPT-3.
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  • Identify relevant hackathons or competitions
  • Form a team or work individually
  • Develop a project idea that utilizes GPT-3
  • Implement the project and prepare for submission
  • Present the project and compete against other participants
Show all three activities

Career center

Learners who complete OpenAI & ChatGPT API's: Expert Fine-tuning for Developers will develop knowledge and skills that may be useful to these careers:
Computational Linguist
As a Computational Linguist, you will use your expertise in computer science and linguistics to develop models that can understand and generate human language. You will work on a variety of projects, such as developing chatbots, machine translation systems, and text summarization tools. Most in this role have a PhD in computational linguistics or a related field. The OpenAI & ChatGPT API Expert Fine-tuning for Developers builds a foundation in linguistics specially for computers.
NLP Engineer
As an NLP Engineer, you will design, develop, and maintain natural language processing (NLP) models. You will use your expertise in computer science, linguistics, and mathematics to build models that can understand and generate human language. Most in this role have a master's degree in computer science or a related field. This OpenAI & ChatGPT API Expert Fine-tuning for Developers course builds a foundation in NLP which can help prepare you for this role.
Data Scientist
As a Data Scientist, you will use advanced statistical modeling, machine learning, and predictive analytics to transform raw data into actionable insights. You will help identify trends, risks, and opportunities for your organization. Most in this role have a master's degree in computer science and 70% have a PhD. This OpenAI & ChatGPT API Expert Fine-tuning for Developers course builds a foundation for understanding how to use AI tools in your role.
Natural Language Processing Specialist
As a Natural Language Processing Specialist, you will use your expertise in computer science, linguistics, and mathematics to build models that can understand and generate human language. You will work on a variety of projects, such as developing chatbots, machine translation systems, and text summarization tools. Many in this role have a master's degree in computer science or a related field. The OpenAI & ChatGPT API Expert Fine-tuning for Developers course may help you in this role.
Conversational AI Engineer
As a Conversational AI Engineer, you will design, develop, and maintain conversational AI systems. You will work on a variety of projects, such as developing chatbots, virtual assistants, and interactive voice response systems. Many in this role have a bachelor's degree in computer science or a related field. The OpenAI & ChatGPT API Expert Fine-tuning for Developers course may help you build a foundation in conversational AI.
Machine Learning Engineer
As a Machine Learning Engineer, you will be responsible for designing, building, and maintaining machine learning models. You will use your expertise in computer science and mathematics to solve complex problems and develop innovative solutions. Many in this role have a master's or PhD in computer science or computer engineering. This OpenAI & ChatGPT API Expert Fine-tuning for Developers course may help you understand the construction of AI models.
AI Researcher
As an AI Researcher, you will conduct research in the field of artificial intelligence. You will develop new algorithms and techniques for solving complex problems. You will also publish your findings in academic journals and conferences. Most in this role have a PhD in computer science or a related field. The OpenAI & ChatGPT API Expert Fine-tuning for Developers course may help build your foundational understanding of AI.
AI Software Engineer
As an AI Software Engineer, you will design, develop, and maintain software for artificial intelligence applications. You will work on a variety of projects, such as developing chatbots, machine translation systems, and self-driving cars. Many in this role have a bachelor's degree in computer science or a related field. The OpenAI & ChatGPT API Expert Fine-tuning for Developers course may help build your foundational understanding of AI.
AI Analyst
As an AI Analyst, you will lead and direct development efforts in the emerging field of artificial intelligence (AI). Your work requires the ability to identify business problems and determine how AI can solve them. You will design the appropriate programs, implement them, and evaluate their efficacy. Many in this role have a master's degree in computer science or a related discipline. An OpenAI & ChatGPT API Expert Fine-tuning for Developers course may be a useful component of your coursework.
Data Engineer
As a Data Engineer, you will be responsible for designing, building, and maintaining the infrastructure that stores and processes data. You will work on a variety of projects, such as developing data pipelines, data warehouses, and data lakes. Many in this role have a bachelor's degree in computer science or a related field. The OpenAI & ChatGPT API Expert Fine-tuning for Developers course could be a useful supplement in your learning.
UX Designer
As a UX Designer, you will design the user interface for software and hardware products. You will work with engineers and product managers to ensure that the user interface is easy to use and visually appealing. Most in this role have a bachelor's degree in design or a related field. The OpenAI & ChatGPT API Expert Fine-tuning for Developers could be a useful addition to your knowledge and skills, as AI chatbots and conversational agents become more popular and increase the need for UX Designers with a solid grounding in AI.
Software Engineer
As a Software Engineer, you will design, develop, and maintain software applications. You will work on a variety of projects, such as developing web applications, mobile applications, and desktop applications. Most in this role have a bachelor's degree in computer science or a related field. The OpenAI & ChatGPT API Expert Fine-tuning for Developers course may supplement your learning, particularly with the increasing inclusion of AI and chatbots into the field of software engineering.
Web Developer
As a Web Developer, you will design, develop, and maintain websites. You will work on a variety of projects, such as developing e-commerce websites, informational websites, and social media websites. Many in this role have a bachelor's degree in computer science or a related field. The OpenAI & ChatGPT API Expert Fine-tuning for Developers course may aid in your learning, allowing you to incorporate more sophisticated AI elements into your web development projects.
Product Manager
As a Product Manager, you will be responsible for managing the development and launch of new products. You will work with engineers, designers, and marketers to ensure that the product meets the needs of the market. Many in this role have an MBA or a related degree. The OpenAI & ChatGPT API Expert Fine-tuning for Developers course may not be an absolute requirement, but the knowledge gained could be helpful when considering AI-based products.
Technical Writer
As a Technical Writer, you will create documentation for software and hardware products. You will work with engineers and product managers to ensure that the documentation is accurate and easy to understand. Most in this role have a bachelor's degree in English or a related field. The OpenAI & ChatGPT API Expert Fine-tuning for Developers course may not be an absolute requirement for this role, but if writing about AI becomes part of your job, it could prove a useful resource.

Reading list

We've selected 16 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in OpenAI & ChatGPT API's: Expert Fine-tuning for Developers.
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Good choice for learners who want to learn more about computer science, mathematics, and computational theory. The book has been called an “extremely well-written exposition”, one that honors Turing’s work in what has become known today as computer science.
Collaboration between Stephen Hawking and Leonard Mlodinow that explores the nature of the universe and the laws that govern it.
Offers an accessible guide to the strange and mysterious world of quantum physics, perfect for those who want to learn more about the fundamental nature of reality.
Sweeping exploration of the history of the universe, from its origins to its possible future fate.
Provides an exploration of the future of the human mind and its potential.
Provides an in-depth look at the relationship between quantum mechanics and general relativity, and the ongoing debate over black holes, making it a valuable resource for those interested in the nature of reality.
Great resource for learners who want a better understanding of logic and mathematics through the lens of computer science.

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