Excerpts of The Introduction

Who this book is for?

This book offers more than just knowledge; it holds the power to transform you. As you delve into its pages, you’ll unlock insights that propel you into the ranks of the world’s top 10% candidates. It’s not just about learning; it’s about undergoing a profound evolution that sets you apart from the rest. Embrace this opportunity for growth and seize your place among the elite. The following topics will be covered in this book:

• Open AI and its APIs
• Prompt Engineering: From basics to deep dive
• Integrating ChatGPT using code: Practical implementation from the official OpenAI cookbook
• LangChain: A deep dive into the intuitive open-source framework
• Retrieval, Text Embeddings, and Vector Stores

LangChain, a relatively new framework that emerged just two months ago as of this writing, has quickly become the talk of the town and the framework of choice for ChatGPT gurus. It presents an intuitive and open-source solution designed to streamline the development process of applications utilizing large language models (LLMs) like OpenAI or Hugging Face. With LangChain, you gain the ability to construct dynamic and data-responsive applications, taking advantage of the latest advancements in natural language processing. Unlock the potential of LLMs and embark on a journey of innovation with the power of LangChain at your disposal.

Embeddings are numerical representations of data, typically used in natural language processing and machine learning tasks. They map complex data, such as words or entities, into lower-dimensional vector spaces. These vector representations capture semantic relationships between data points, making it easier for algorithms to process and analyze them effectively. Embeddings help systems understand and work with data more efficiently, facilitating tasks like language translation, sentiment analysis, and recommendation systems.

A Vector Store, on the other hand, is a data storage and retrieval system that stores vectors, typically embeddings or numerical representations of various objects. It provides a way to efficiently index and search for data points based on their vector representations, enabling tasks like similarity search and recommendation in large datasets. Vector stores are crucial in applications where finding similar items or entities is essential, such as content recommendation and information retrieval systems.

How is this book organized?

This book caters to two types of readers: those who prefer hands-on practice and those who seek a clear, engaging understanding of ChatGPT concepts. Whether you’re eager for practical exercises or prefer a fun, plain English approach to ChatGPT, this book accommodates both preferences. It is organized into three parts. Part I is beginner-friendly and requires no prior programming skills, gradually delving into Prompt Engineering concepts. Part II and Part III assume intermediate-level Python programming skills, and while some sections may appear challenging, we provide step-by-step coding explanations. If you’re new to programming, I offer a complimentary companion book titled “Python Programming Foundation For AI”, ensuring that you have the Python skills needed to follow along with the codes in both Part II and Part III of this book.

Bloom’s Taxonomy and Bottom-Up vs Top-Down approach

Bloom’s Taxonomy, a widely acknowledged hierarchical classification of cognitive abilities, plays a significant role in distinguishing between lower and higher-level cognitive skills in the realm of education.

In 1956, Benjamin Bloom, together with Max Englehart, Edward Furst, Walter Hill, and David Krathwohl, presented a groundbreaking framework for organizing educational objectives known as Bloom’s Taxonomy. This framework has been adopted by numerous K-12 educators and college instructors to improve their pedagogical techniques. The taxonomy consists of six primary categories: Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation. With Knowledge as the foundation, the other categories emphasize “skills and abilities” that can be developed and employed based on the prerequisite knowledge. Although each category contains subcategories, they all lie on a continuum ranging from simple to complex and concrete to abstract, facilitating educators’ understanding and implementation of the framework.

The “bottom-up” approach, a standard method of learning new topics or concepts in various educational environments, follows the progression of Bloom’s Taxonomy. It begins with introducing new terms, understanding the concept, applying, or identifying the concept in a specific example, and ultimately analyzing and evaluating scenarios related to the concept. If fortunate, learners might even have the opportunity to create something using the concept in context. However, this method can often be tedious for learners, especially when the subject matter lacks excitement.

A “top-down” approach offers an alternative way to employ Bloom’s Taxonomy in education. This method starts with higher-level activities and examples, utilizing the challenges and puzzles associated with these activities to introduce and reinforce new concepts and terminology along the way. For instance, in sports, a coach might initially demonstrate a complex play and then break it down into smaller components. This enables players to better comprehend how each individual component contributes to the bigger picture while learning the necessary terminology and concepts throughout the process.

Building on the top-down approach, we will first engage you in applying technical concepts and programming techniques, sparking excitement and interest. Then, we will delve into the inner workings and detailed explanations of each section, allowing you to gain a more profound and meaningful understanding.

By implementing this top-down approach, you will be introduced to the practical applications of various technical concepts and programming methods from the onset. This hands-on experience will not only stimulate your curiosity but also help you grasp the relevance of the subject matter. Once you are captivated by the practical aspects, we will gradually unveil the underlying mechanisms and provide comprehensive explanations for each component. This method ensures a more engaging and effective learning experience that connects theory with real-world applications.