This specialization is a quick start guide to help people use and launch LLMs like GPT, Llama, T5, and BERT at scale. It presents a step-by-step approach to building and deploying LLMs, with real-world case studies to illustrate the concepts, and covers topics such as constructing agents, fine-tuning a Llama 3 model with RLHF, building recommendation engines with Siamese BERT architectures, launching an information retrieval system with OpenAI embeddings and GPT-4, and building an image captioning system with the vision transformer and GPT. This guide provides clear instructions and best practices for using LLMs and will be a valuable resource for anyone looking to use LLMs in their projects.

Applied Learning Project

Learners will apply large language models like GPT, Llama, and BERT to build real-world AI systems, including a recommendation engine, a semantic search platform, a retrieval-augmented generation (RAG) chatbot, and a multimodal image captioning model. Each project reinforces core skills such as prompt engineering, fine-tuning, embeddings, and deploying production-ready LLM applications.