This complete LLM engineering course provides a practical and structured introduction to working with Large Language Models (LLMs) and building real AI applications. It is designed for developers, engineers, and learners who want to move from theory to hands-on implementation.
The course starts with a clear introduction to LLMs, explaining how they work and how they can be used in real-world applications. You will learn how to interact with models using the OpenAI API, write effective prompts, and understand the basics of prompt engineering for solving practical problems.
It then covers powerful development tools like the Hugging Face Transformers library, including how to build chatbots and AI interfaces using Python and Gradio. You will also explore text embeddings, semantic search, and classification techniques used in modern AI systems.
Advanced topics include fine-tuning large language models, including efficient methods like QLoRA for training models on limited hardware. The course also explains Retrieval-Augmented Generation (RAG), which improves AI responses by connecting models to external data sources.
Finally, learners will discover how to build custom AI assistants using different approaches such as RAG, tool usage, and fine-tuning. By the end of the course, you will be able to design, build, and optimize real-world AI applications using LLMs.
