AI Projects That Get You Hired

Build Your Own GPT Copilot: Using Web Scraping,
PDF Ingestion and Multi-Tenant Features

Sam Reed

This book was inspired by friends and colleagues eager to break into the AI job market but trapped in a frustrating cycle: no job without experience, no experience without a job. To help them—and anyone in a similar situation—I created the series AI Projects That Get You Hired. This series is built around hands-on, real-world projects that provide the practical skills and confidence needed to jumpstart a career in AI, even without prior industry experience.

Drawing on my five years of research at Stanford and over fifteen years in the AI industry, Build Your Own Scalable U-Copilot stands as the first project in this series.

This book is more than just a coding manual; it's a strategic guide to advancing your AI career. By creating a scalable GPT copilot, you will not only uncover the vast potential of AI but also harness its power to elevate your professional journey.

What is in this book?

GitHub Repository: Git clone a working GPT Copilot repository, U-Copilot, in action—an interactive assistant that demonstrates querying websites or PDF/text files for meaningful insights. This chapter gives you a firsthand look at the capabilities of a fully operational LLM.
From Setup to Launch: Learn the step-by-step process to set up your development environment and integrate essential tools to efficiently launch your GPT project.
Multi-Tenant Application: Building on the foundation laid in the previous chapter, this chapter guides you through the customization of your GPT application to accommodate multiple tenants.
Tokenizer, Embedding, and Retrieval: Explore how tokenizers dissect text, embeddings represent linguistic details numerically, and retrieval processes use these representations to access relevant information, equipping developers with foundational knowledge for applying LLMs in real-world applications.
LLM & RAG Essentials: Gain a thorough understanding of the foundational concepts of Large Language Models and Retrieval-Augmented Generation. These core skills are crucial for deploying effective real-world LLM applications.
System Workflow: Master the end-to-end workflow of a GPT Copilot—from handling user queries to generating nuanced responses. Learn to incorporate embeddings and vector databases effectively to enhance functionality.
Core Components Deep Dive: Delve deep into the technical implementation of essential features such as the integration with vector databases (e.g., Qdrant) and the use of embeddings for semantic search, alongside practical uses of LLM models (e.g., Mistral) for dynamic response generation.