Prompt Engineering & Fundamentals of Generative AI and LLMs

Prompt Engineering, Generative AI & LLM Models Fundamentals course is designed for learners who want to build a strong foundation in Large Language Models (LLMs), Generative AI concepts, and prompt engineering techniques. The course focuses on helping technical professionals and AI enthusiasts understand how modern generative AI systems work and how to effectively interact with and optimize these models for real-world applications. This course bridges the gap between theoretical knowledge of generative AI and practical techniques used to guide, evaluate, and improve LLM performance. Learners will explore how LLMs are trained on large datasets, how they generate responses, and how prompt engineering and fine-tuning techniques can be applied to improve the quality and reliability of AI outputs. This course facilitates learners with approximately 4:00–5:00 hours of video lectures, providing a comprehensive understanding of both core LLM concepts and practical prompt engineering strategies. The course is structured into 3 comprehensive modules, with each module further divided into focused technical lessons. To test learners’ understanding, every module includes quizzes and in-video knowledge checks. Enroll in our “Prompt Engineering, Generative AI & LLM Models Fundamentals” course to develop the skills needed to design effective prompts, understand LLM training processes, and apply advanced techniques used in modern generative AI systems. Modules Included in the Course Module 1: Foundations of Large Language Models and Generative AI Module 2: LLM Training, Optimization, and Evaluation Module 3: Prompt Engineering, Fine-Tuning, and Advanced LLM Architectures This course is specifically designed for technical professionals, developers, AI practitioners, and learners interested in understanding the core mechanisms behind generative AI systems and LLM-based applications. By the end of this course, a learner will be able to: Understand the fundamental concepts of Large Language Models and Generative AI systems. Explain how LLMs are trained, optimized, and evaluated using different learning techniques and metrics. Apply prompt engineering and prompt design techniques to guide model outputs effectively. Understand advanced techniques such as fine-tuning, prompt tuning, and Retrieval-Augmented Generation (RAG) used to improve LLM performance

• Understand the fundamentals of Large Language Models and Generative AI
• Apply prompt engineering to guide LLM outputs and improve response quality
• Explore model optimization techniques including fine-tuning, evaluation metrics, and Retrieval-Augmented Generation

• Basic familiarity with the topic and terminology
• Readiness to practice with applied exercises or case studies