Generative AI and Model Selection: Choosing the Right LLMs

With the rapid rise of Generative AI, choosing the right model for your specific needs can be overwhelming. Understanding how to evaluate different architectures, sizes, and capabilities is essential to building efficient and cost-effective AI solutions. This text-only course guides you through the foundational concepts of Generative AI and provides a structured framework for model selection. You will transition from understanding basic AI terminology to confidently analyzing model trade-offs, estimating costs, and matching models to real-world tasks. What you'll learn: - Understand foundational Generative AI concepts, terminology, and model architectures. - Evaluate major Large Language Models (LLMs) based on benchmarks, context windows, and task suitability. - Analyze key trade-offs between open-source and proprietary models, including privacy and customization. - Calculate operational factors such as token pricing, latency, and computational requirements. - Apply modern selection frameworks to choose models for specific patterns like Retrieval-Augmented Generation (RAG). You will start with core definitions and basic terminology before exploring evaluation metrics, benchmark datasets, and practical selection matrices. Through written scenarios and comparative analyses, you will learn how to align technical capabilities with business constraints. Designed for beginners, developers, and product managers looking to integrate AI, this course requires no prior machine learning experience. Begin reading today to master the art of choosing the perfect model for your next AI project.