This practical course on Large Language Models (LLMs) introduces learners to modern AI development workflows using OpenAI APIs, Hugging Face Transformers, prompt engineering, and custom AI assistant design. It is ideal for developers, AI enthusiasts, and learners interested in building real-world generative AI applications.
The course begins with a practical introduction to LLMs, explaining how transformer-based models process language, generate responses, and power modern AI systems such as chatbots and intelligent assistants.
Learners are introduced to the OpenAI Python API, including how to integrate AI capabilities into applications using Python code. The course demonstrates real examples of generating text, interacting with models, and building AI-powered workflows.
A major focus is on the Hugging Face Transformers library, where learners explore pretrained models, inference pipelines, and chatbot interfaces using tools like Gradio for rapid UI development.
The training also covers prompt engineering techniques that help improve AI outputs through better instructions, structured prompts, and contextual guidance.
Advanced sections explain how to fine-tune LLMs for specialized tasks, including practical approaches such as QLoRA for efficient fine-tuning on limited hardware resources.
Additionally, learners explore how to build custom AI assistants using Retrieval-Augmented Generation (RAG), tool integration, and fine-tuning workflows.
The course also provides high-level insights into building LLMs from scratch, including training concepts, model architecture, and data processing pipelines.
By the end of this course, learners will understand practical LLM development, API integration, prompt engineering, model fine-tuning, and the c
