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Complete OpenAI API Masterclass for Beginners using Python

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Based on 26 ratings
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Pragmatic Code School

"AI Application Development with OpenAI, ChatGPT, and Python" is a comprehensive course designed to teach you how to harness the power of OpenAI's APIs and tools to build advanced AI applications.

  • You'll explore the fundamentals of Large Language Models (LLMs), understand the evolution of ChatGPT, and gain hands-on experience in using OpenAI's capabilities for text, image, and audio processing.

  • The course covers essential topics such as prompt engineering, structured data generation, and function calling, enabling you to create dynamic and interactive AI solutions.

Read more

"AI Application Development with OpenAI, ChatGPT, and Python" is a comprehensive course designed to teach you how to harness the power of OpenAI's APIs and tools to build advanced AI applications.

  • You'll explore the fundamentals of Large Language Models (LLMs), understand the evolution of ChatGPT, and gain hands-on experience in using OpenAI's capabilities for text, image, and audio processing.

  • The course covers essential topics such as prompt engineering, structured data generation, and function calling, enabling you to create dynamic and interactive AI solutions.

Whether you're a developer, data scientist, or AI enthusiast, this course provides the knowledge and skills to develop cutting-edge AI applications using Python and OpenAI.

1. Getting Started with the Course

This section introduces the course, outlining what you can expect to learn and achieve by the end of it. We will cover how to set up your environment, download course materials, and access the resources needed to follow along.

2. Introduction to Large Language Models (LLMs), OpenAI & ChatGPT

Dive into the world of LLMs with an in-depth look at OpenAI's ChatGPT, its architecture, and how it’s revolutionizing AI-driven language processing. Explore the history of how we interacted with computers before LLMs and how it has evolved since the release of ChatGPT. You'll trace the evolution of LLMs and understand the complexities involved in training these models.

3. OpenAI APIs: Your First Steps to Mastery

Master the essential steps for working with OpenAI APIs, from setting up your environment on Mac or Windows to making your first API requests. This section covers everything from installing Python, managing dependencies using Poetry or pip, configuring your OpenAI API key, and interacting with GPT models using OpenAI clients.

4. Mastering Multimodality: Creating and Editing Images with OpenAI

Learn how to leverage OpenAI's capabilities to generate and edit images. This section introduces you to multimodality in AI, combining text and image generation. You’ll explore how to create images, edit them, and use OpenAI's variation functions to enhance creativity.

5. Mastering Multimodality: Exploring Vision Capabilities with OpenAI

Delve into the vision capabilities of OpenAI. You'll learn how to analyze images using URLs, process base64-encoded images, and understand the limitations of OpenAI’s Vision API. This knowledge will help you integrate vision-based AI solutions into your projects.

6. Mastering Multimodality: Creating and Processing Audio with OpenAI

Explore how OpenAI handles audio data, including text-to-speech conversion, speech-to-text transcription, and language translation using the Whisper API. You will gain hands-on experience in converting written text into speech and transcribing spoken language into text.

7. Prompt Engineering

This section covers the art of crafting prompts to guide AI models in generating accurate outputs. You’ll learn about various prompting techniques, including zero-shot and few-shot prompting, and how to structure prompts to achieve desired results. You’ll also explore how to protect prompts from injection attacks.

8. Generating Structured Data with OpenAI

Understand how to generate structured data using OpenAI's LLMs. This section includes prompt engineering techniques, using Pydantic for data validation, and advanced methods for structured outputs. You'll learn how to manage structured data in Python efficiently and how to combine Pydantic with prompt engineering for accurate data generation.

9. Function Calling using Tools with OpenAI

Discover how to use OpenAI for function calling to interact with external systems, retrieve real-time data, and build interactive applications. You'll learn how to connect OpenAI to APIs for real-time data, such as weather updates and stock prices, making your AI applications more dynamic and responsive.

This comprehensive course will equip you with the skills needed to build AI-powered applications using OpenAI's powerful tools and APIs. Join us as we embark on this journey to master the art of AI application development with hands-on projects and real-world examples.

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What's inside

Learning objectives

  • Learn to interact with openai platform (generative ai) using python code
  • Learn the llm basics, chatgpt evolution, training, and practical usage.
  • Learn to work and explore the multimodal capabilities such as images, files, audio using openai and python code.
  • Learn to use prompt engineering to guide ai models in generating accurate outputs.
  • Learn to use latest techniques to generate the structured outputs from llm
  • Learn to use the power of function calling with openai to interact with external systems
  • Build a chatbot using streamlit
  • Explore the features of openai canvas, a modern collaborative tool for writing code.

Syllabus

This lecture explains the top_p parameter and its role in controlling response randomness.

Getting Started With the Course

In this lecture, I will give an introduction to the course and highlight the topics that are covered in this course.

Read more

In this lecture, I will cover the prerequisites that are needed for this course.

Explore key concepts visually with comprehensive course slides that provide an organized overview of the material.

Get hands-on experience with source code examples that bring the course content to life through practical implementation.

Explore the pre-LLM era, How we interact with computers for solutions ? How its changed since the release of ChatGPT.

Trace the evolution of Large Language Models, from early iterations to the cutting-edge architectures shaping today's AI landscape.

Delve into the training process of LLMs, covering data preprocessing, model training, fine-tuning, and the computational challenges involved.

Discover the journey of GPT models, from the inception of GPT-1 to the advancements in GPT-4, highlighting key innovations and improvements.

Examine the benefits, hurdles, and diverse applications of LLMs, showcasing their impact across various industries and use cases.

Learn how to sign up for ChatGPT, navigate its features, and begin experimenting with this powerful AI tool in practical scenarios.

This lecture provides an overview of the OpenAI API and its key capabilities.

Learn how to create an OpenAI account and explore the capabilities of the OpenAI Playground.

This lecture guides you through setting up Python on a Mac for OpenAI API development.

Learn how to install and configure Python on Windows for working with OpenAI APIs.

Discover how to initialize a base project for OpenAI API using Python’s Poetry dependency manager.

This lecture shows how to securely configure your OpenAI API key in your project.

Learn how to interact with GPT models by sending requests through the OpenAI client.

This lecture focuses on techniques to refactor and improve the readability of your OpenAI API code.

Understand how temperature and max_tokens parameter influence the behavior of GPT responses.

Understand how max_tokens parameters influence the behavior of GPT responses.

Explore the concepts of prompts, tokens, and tokenization and how they impact API interactions.

Learn how to stream responses from OpenAI for real-time interaction with the API.

This lecture introduces the roles of system, assistant, and user messages in shaping AI responses.

See how system, assistant, and user messages work in practice through real-world examples.

An overview of multimodality in AI, integrating text and image generation for versatile applications

Learn how to create images using OpenAI’s image generation API with step-by-step guidance

In this lecture, you'll learn how to refactor your code to efficiently save images to the file system, ensuring clean and optimized handling of image data in your applications.

Discover how to create image variations using OpenAI's create_variation function for enhanced creativity.

This lecture explores how OpenAI processes images via URLs to generate descriptions, perform object recognition, and analyze visual content.

Learn how OpenAI models interpret base64-encoded images to extract visual features and process data directly within requests.

Examine the constraints and challenges associated with OpenAI's Vision API, including limitations in image size, processing, and capabilities.

Learn how to use OpenAI’s TTS model to convert written text into realistic, natural-sounding speech.

Discover how Whisper API can transcribe spoken language into accurate, readable text in various languages.

Understand how Whisper API handles translations, transforming spoken French into English with accuracy and ease.

This lecture teaches how to create clear, structured prompts that lead to accurate and meaningful AI outputs.

Learn how to set up your project environment, ensuring all tools and libraries are ready for effective prompt engineering.

Dive into a practical example by creating a travel plan prompt, showcasing prompt engineering techniques in a real-world scenario.

Explore the concept of prompt injection, its potential risks, and effective strategies to protect your prompts against manipulation.

This lecture explains zero-shot and few-shot prompting techniques to improve AI performance with minimal examples.

Learners will explore how Chain of Thought Prompting helps guide AI through logical, step-by-step problem-solving.

Learn how to craft and utilize multi-step prompts to guide LLMs through complex tasks, achieving more detailed and accurate responses.

Understand how LLMs generate structured data and why it's crucial for AI-driven applications.

Explore how prompt engineering can guide LLMs to create structured and organized outputs efficiently.

Learn how few-shot examples enhance prompt engineering to produce more accurate structured outputs.

Dive into Pydantic and see how it helps in defining, validating, and managing structured data in Python.

Discover how to combine Pydantic and prompt engineering for robust structured output generation and validation.

Learn to leverage response_format with Pydantic to generate well-structured, validated data effortlessly.

Understand the fundamentals of function calling and why it's essential for AI integration. Discover how OpenAI uses function calling to access and manipulate external data.

Learn how to use OpenAI's function calling to fetch system details like name and time. This lecture demonstrates how AI can interact with your device for dynamic information retrieval.

Create a command-line application that takes user input and iterates seamlessly. Understand how to implement function calling for dynamic, interactive experiences.

Integrate OpenAI function calling with the Open Meteo API to fetch real-time weather data. Gain hands-on experience in using AI to interact with external weather services.

Learn to connect OpenAI with financial APIs to retrieve live stock prices. This lecture showcases how to leverage AI for real-time financial data integration.

Get started with Streamlit by building a basic chatbot that interacts with users through a clean and simple UI.

Build a simple chatbot using Streamlit.

Upgrade your chatbot to stream responses for a real-time conversational experience.

Implement chat history to display previous interactions, enriching the user experience.

Explore OpenAI's Canvas and learn how to enhance chatbot interactions with a dynamic UI.

Implement a sidebar to keep track of chatbot conversations for a seamless user experience.

Get familiar with SQLite3 and understand its role in lightweight data storage.

Learn how to store chatbot conversations in an SQLite database for persistence.

Learn how to enable image processing in your chatbot, allowing users to upload and analyze images seamlessly.

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Provides hands-on experience using OpenAI's capabilities for processing text, images, and audio, which are essential skills for building modern AI applications
Covers prompt engineering techniques, which are crucial for guiding AI models to generate accurate and relevant outputs, enhancing the reliability of AI-driven solutions
Explores function calling, enabling interaction with external systems and real-time data retrieval, which is valuable for creating dynamic and responsive AI applications
Teaches how to generate structured data using OpenAI's LLMs and Pydantic for data validation, which is useful for managing data in Python efficiently
Requires setting up an OpenAI account and API key, which may involve costs depending on usage and could be a barrier for some learners
Uses tools such as Streamlit and SQLite, which may require learners to install and configure additional software, potentially adding complexity to the setup process

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Reviews summary

Mastering openai api fundamentals with python

According to learners, this course serves as a solid foundation for anyone looking to get started with the OpenAI API using Python. Many students highlight the course's practical examples and hands-on approach as particularly effective, appreciating the coverage of various modalities like text, image, and audio processing, as well as essential topics like prompt engineering and function calling. While largely positive, some reviewers note that due to the rapid pace of AI development, certain sections or code examples can become outdated quickly, which may require learners to consult the latest OpenAI documentation.
Course subject matter changes fast.
"It's challenging for any course in this area to stay perfectly up-to-date because the tech changes so rapidly."
"This course provides a snapshot of the API at a certain time; continuous learning outside the course is necessary."
"Learned the core concepts, but be prepared to look up the very latest API parameters yourself."
Instructor explains concepts clearly.
"The instructor does a great job of explaining complex concepts in a clear and understandable way."
"Really liked the teaching style; it was easy to follow along with the explanations."
"The instructor's passion for the topic comes through, making the lectures engaging."
Explores text, image, and audio.
"Appreciated that the course covered not just text generation but also working with images and audio through the API. Very comprehensive."
"It was great to see how the API handles different data types like text, images, and sound. Opens up a lot of possibilities."
"The sections on image and audio APIs were particularly interesting and well-explained."
Focuses on practical coding examples.
"The hands-on coding examples and projects are the strongest part of the course for me. Actually writing code really solidified my understanding."
"Loved that it wasn't just theory. We got to build real things using the API right away."
"The practical exercises make learning the API feel very real and applicable to future projects."
Great starting point for OpenAI API.
"This course provides an excellent foundation for getting started with OpenAI's APIs using Python. The explanations are clear and easy to follow..."
"For anyone starting out with the OpenAI API, this course is a fantastic resource. It covers all the basics you need to know to begin building."
"I was a complete beginner to the OpenAI API, and this course made it very approachable. I feel confident now to experiment on my own."
Some code examples are outdated.
"Due to the fast-changing nature of the OpenAI API, some code examples were slightly outdated and required minor adjustments to work."
"Had some issues getting the code to run directly because the API had changed since the lectures were recorded."
"Found myself having to check the official documentation occasionally to adapt the provided code examples."

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 Complete OpenAI API Masterclass for Beginners using Python with these activities:
Review Python Fundamentals
Reinforce your understanding of Python syntax and data structures. This will provide a solid foundation for working with the OpenAI API.
Browse courses on Python Basics
Show steps
  • Review basic data types (strings, integers, lists, dictionaries).
  • Practice writing simple functions and control flow statements.
  • Familiarize yourself with Python's standard library.
Read 'Python Crash Course'
Gain a stronger foundation in Python programming. This book will help you understand the syntax and concepts used in the course.
View Python Crash Course, 2nd Edition: A Hands-On,... on Amazon
Show steps
  • Read the first few chapters covering basic Python syntax and data structures.
  • Work through the example projects to practice your skills.
Practice Prompt Engineering
Refine your prompt engineering skills. Experiment with different prompts to understand how they influence the AI model's output.
Show steps
  • Experiment with different prompting techniques (zero-shot, few-shot).
  • Analyze the AI model's responses and adjust your prompts accordingly.
  • Try to protect prompts from injection attacks.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Build a Simple Chatbot
Apply your knowledge of the OpenAI API and Python to build a chatbot. This will help you understand how to integrate the API into a real-world application.
Show steps
  • Set up a basic chatbot using Streamlit.
  • Integrate the OpenAI API to generate responses.
  • Implement chat history to improve the user experience.
Read 'Building Applications with Generative AI'
Expand your knowledge of generative AI and learn how to build more complex applications. This book will provide you with the tools and techniques you need to succeed.
View RAG-Driven Generative AI: Build custom... on Amazon
Show steps
  • Read the chapters covering advanced prompt engineering techniques.
  • Explore the examples of building different types of applications with generative AI.
Document Your Chatbot Project
Solidify your understanding by documenting your chatbot project. This will help you organize your thoughts and identify areas where you need to improve.
Show steps
  • Write a README file explaining the purpose and functionality of your chatbot.
  • Document the code with comments and docstrings.
  • Create a short video demonstrating your chatbot.
Contribute to an OpenAI Project
Deepen your understanding by contributing to an open-source project that uses the OpenAI API. This will expose you to different coding styles and best practices.
Show steps
  • Find an open-source project that uses the OpenAI API.
  • Identify a bug or feature that you can contribute to.
  • Submit a pull request with your changes.

Career center

Learners who complete Complete OpenAI API Masterclass for Beginners using Python will develop knowledge and skills that may be useful to these careers:
AI Application Developer
An AI Application Developer builds and implements AI-powered solutions in various applications. This role often involves leveraging APIs and SDKs to integrate AI functionalities into existing software or create new AI-driven applications. This course equips you with the skills to use OpenAI's APIs for text, image, and audio processing, which form the backbone of many AI applications. Through the course, you will learn to master multimodality by creating as well as editing images and processing audio with OpenAI. Furthermore, the knowledge gained in prompt engineering and structured data generation directly enhances your ability to develop robust and effective AI applications as an AI Application Developer.
See salaries and explore the career path for AI Application Developer
Prompt Engineer
A Prompt Engineer specializes in crafting effective prompts to guide AI models, particularly large language models, to generate accurate and relevant outputs. This career involves experimenting with different prompting techniques, such as zero-shot and few-shot prompting, to optimize AI responses. This course helps in becoming a Prompt Engineer by providing a dedicated section on prompt engineering. You will learn how to structure prompts, protect them from injection attacks, and use advanced techniques to achieve desired results. The skills to generate structured data using OpenAI's LLMs that are taught in this course may be particularly useful.
See salaries and explore the career path for Prompt Engineer
Chatbot Developer
A Chatbot Developer creates conversational AI interfaces that enable users to interact with systems using natural language. This involves designing the chatbot's architecture, implementing natural language processing (NLP) algorithms, and integrating it with various platforms. This course prepares you for a Chatbot Developer role by providing hands-on experience in building a chatbot using Streamlit. Additionally, you will gain practical knowledge of using OpenAI's APIs for natural language understanding and generation, which are essential components of any sophisticated chatbot. Mastering function calling with OpenAI, as covered in the course, allows you to integrate real-time data and external systems into your chatbot.
See salaries and explore the career path for Chatbot Developer
AI Solutions Architect
An AI Solutions Architect designs and oversees the implementation of AI solutions within an organization. This role requires a deep understanding of AI technologies, system architecture, and business requirements to create effective and scalable AI solutions. This course helps in becoming an AI Solutions Architect by providing a broad understanding of OpenAI's APIs and their capabilities. You'll learn how to integrate these APIs into various applications and design solutions that leverage AI for text, image, and audio processing. The knowledge gained in prompt engineering and function calling equips you with the skills to design intricate AI systems.
See salaries and explore the career path for AI Solutions Architect
AI Consultant
An AI Consultant advises organizations on how to leverage AI technologies to improve their operations and achieve their business goals. This role involves assessing the client's needs, identifying opportunities for AI adoption, and recommending the appropriate AI solutions. This course provides you with the knowledge and skills to become an AI Consultant by exposing you to a wide range of AI applications and techniques using OpenAI's APIs. You'll gain hands-on experience in prompt engineering, structured data generation, and function calling, enabling you to recommend practical and effective AI solutions to clients. The range of topics covered, from text to image to audio processing, may be attractive.
See salaries and explore the career path for AI Consultant
Data Scientist
A Data Scientist analyzes large datasets to extract insights, build predictive models, and solve complex problems. The role requires expertise in statistical analysis, machine learning, and data visualization. This course may be useful to Data Scientists looking to expand their skills in natural language processing and generative AI. The course teaches you how to leverage OpenAI's APIs for various tasks, such as text generation, image analysis, and audio processing. You will also learn to generate structured data using LLMs, which can be invaluable for data analysis and model building.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models and systems. This role involves designing machine learning pipelines, training models, and optimizing their performance. This course may be useful as it provides practical experience in using OpenAI's APIs, which can be integrated into machine learning workflows. You will learn how to generate and manipulate text, images, and audio using AI models, which can be valuable for various machine learning applications. Additionally, the knowledge of function calling allows you to connect AI models to external systems and data sources.
See salaries and explore the career path for Machine Learning Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This role requires proficiency in programming languages, software development methodologies, and problem-solving skills. Software Engineers can use this course to enhance their skills in AI application development. The course provides hands-on experience in working with OpenAI's APIs and integrating them into software applications. You will learn to leverage AI for tasks such as natural language processing, image generation, and audio processing, adding new capabilities to our software projects.
See salaries and explore the career path for Software Engineer
AI Product Manager
An AI Product Manager defines and manages the development of AI-powered products. This role involves understanding market needs, defining product requirements, and working with engineering teams to deliver successful AI products. This course may be helpful to AI Product Managers by providing a comprehensive overview of OpenAI's APIs and their potential applications. You will gain a better understanding of the capabilities and limitations of these APIs, which can inform product decisions and roadmaps. The hands-on experience in building AI applications will provide valuable insights into the development process.
See salaries and explore the career path for AI Product Manager
Technical Writer
A Technical Writer creates documentation for software, hardware, and other technical products. This role requires strong writing skills, attention to detail, and the ability to understand and explain complex technical concepts. This course can help Technical Writers who want to specialize in AI. The course provides a solid foundation in AI application development using OpenAI's APIs, enabling you to write accurate and informative documentation for AI-powered products and services. The understanding of prompt engineering and structured data generation can be particularly valuable for documenting AI models and their behavior.
See salaries and explore the career path for Technical Writer
AI Trainer
An AI Trainer works on improving the performance of artificial intelligence models through various techniques. This role often involves labeling data, evaluating model outputs, and providing feedback to developers. This course may be useful to AI Trainers by providing a deeper understanding of how AI models work and how they can be improved. You will learn about prompt engineering, which is a critical skill for guiding AI models to generate accurate outputs. The hands-on experience in working with OpenAI's APIs can also provide valuable insights into the model behavior.
See salaries and explore the career path for AI Trainer
Data Analyst
A Data Analyst collects, cleans, and analyzes data to identify trends and insights. This role requires proficiency in data analysis tools and techniques. This course may be helpful to Data Analysts who want to expand their skills in natural language processing and text analysis. You will learn how to use OpenAI's APIs to extract information from text data, generate summaries, and perform sentiment analysis. The knowledge of structured data generation can also be valuable for organizing and analyzing data more effectively.
See salaries and explore the career path for Data Analyst
IT Support Specialist
An IT Support Specialist provides technical assistance to computer users. This role involves troubleshooting hardware and software issues, installing and configuring systems, and providing user training. This course may be useful for IT Support Specialists who want to understand how AI is being used in IT and how to support AI-powered systems. You will gain a basic understanding of OpenAI's APIs and their applications, which can help you troubleshoot issues related to AI software. The knowledge of prompt engineering can also be useful for interacting with AI-powered help desks and chatbots.
See salaries and explore the career path for IT Support Specialist
Digital Marketing Specialist
A Digital Marketing Specialist develops and implements digital marketing campaigns to promote products and services. This role requires expertise in social media marketing, search engine optimization (SEO), and content marketing. This course can help Digital Marketing Specialists who want to leverage AI to improve their marketing efforts. The course can help you use OpenAI's APIs to generate marketing content, personalize customer experiences, and automate marketing tasks. The skills in prompt engineering can be particularly valuable for crafting effective marketing messages.
See salaries and explore the career path for Digital Marketing Specialist
Educator
An Educator teaches students in an academic setting. They are responsible for designing curricula, delivering lessons, and assessing student performance. Educators can use this course to integrate AI into their teaching practices. The course provides a comprehensive overview of OpenAI's APIs and their potential applications in education. You will learn how to use AI to personalize learning experiences, automate grading, and create engaging educational content.
See salaries and explore the career path for Educator

Reading list

We've selected two 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 Complete OpenAI API Masterclass for Beginners using Python.
Cover image
RAG-Driven Generative AI
Save
Provides a comprehensive guide to building applications with generative AI models. It covers various techniques and best practices for using LLMs and other generative models to create innovative applications. It is particularly useful for those who want to go beyond the basics and explore more advanced topics.
RAG-Driven Generative AI: Build custom retrieval...
Kindle Edition
$$
Cover image
Python Crash Course, 2nd Edition
Save
Provides a solid introduction to Python programming. It covers the fundamentals of Python and includes hands-on projects that will help you solidify your understanding. It is particularly useful for those who are new to Python or who want to refresh their skills before diving into the OpenAI API.
Python Crash Course, 2nd Edition: A Hands-On,...
Paperback
$$
Python Crash Course, 2nd Edition: A Hands-On,...
Kindle Edition
$$

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Rating
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Effort
8 total hours
Via
Udemy
Instructor
Pragmatic Code School
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Provides hands-on experience using OpenAI's capabilities for processing text, images, and audio, which are essential skills for building modern AI applications
Covers prompt engineering techniques, which are crucial for guiding AI models to generate accurate and relevant outputs, enhancing the reliability of AI-driven solutions
Explores function calling, enabling interaction with external systems and real-time data retrieval, which is valuable for creating dynamic and responsive AI applications
Teaches how to generate structured data using OpenAI's LLMs and Pydantic for data validation, which is useful for managing data in Python efficiently
Requires setting up an OpenAI account and API key, which may involve costs depending on usage and could be a barrier for some learners
Uses tools such as Streamlit and SQLite, which may require learners to install and configure additional software, potentially adding complexity to the setup process
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