Generative AI Foundations: Prompt Engineering and LLM Development

Generative AI is reshaping how we interact with technology, but understanding the mechanics behind Large Language Models is key to using them effectively. This course provides a structured path to understanding how these models function and how to leverage them for practical use cases. You will transition from a curious observer to a practitioner capable of designing effective prompts and customizing models for specific tasks. By working through written explanations and code examples, you will gain the skills necessary to navigate the rapidly evolving landscape of artificial intelligence. What you'll learn: - Understand the architecture and evolution of Large Language Models - Master prompt engineering techniques to improve model accuracy and reliability - Apply fine-tuning strategies using Hugging Face to specialize models for specific data - Implement Retrieval-Augmented Generation (RAG) to connect models with external information - Explore ethical considerations and safety protocols in AI development - Practice building conversational interfaces and text summarization tools using Python The course begins with fundamental terminology and the history of language modeling before moving into practical implementation using Python and industry-standard libraries. You will explore how to manage model parameters, evaluate outputs, and use vector databases for more complex AI workflows. This course is designed for beginners with a basic understanding of Python who want to enter the world of AI development. No prior experience with machine learning is required. Start building your foundation in generative technology today.