Quick Guide to ChatGPT, Embeddings, and Large Language Models (LLMs) is a fast, practical introduction to understanding, using, and deploying modern language models such as GPT, T5, and BERT. Designed for developers, analysts, and AI practitioners, this guide walks you step-by-step through real-world applications that demonstrate how LLMs can be used at scale to solve meaningful problems.
 
Course Description
This quick-start guide breaks down the essentials of working with LLMs—from core concepts to hands-on deployments. You’ll explore how models like GPT and BERT function, how embeddings unlock semantic search and retrieval workflows, and how transformer-based systems power multimodal AI.
 
Through real case studies, you’ll learn:
How to build recommendation engines using siamese BERT architectures.
How to create information retrieval systems using OpenAI embeddings and GPT.
How to develop image captioning pipelines that combine vision transformers with text-generation models like GPT-J.
 
The guide offers clear explanations, practical tips, and best practices for launching scalable LLM-powered systems, filling a critical gap for learners who want to apply LLMs—not just understand them conceptually.
 
What You’ll Learn
How large language models work and why they’re transforming NLP.
Techniques for generating text, answering questions, and understanding context with LLMs.
How embeddings enable similarity search, semantic retrieval, and ranked recommendations.
Best practices for designing LLM-powered chatbots, retrieval systems, and multimodal pipelines.
Real-world strategies for deploying LLM-based solutions at scale.