Generative AI and Model Selection for Beginners

With so many generative artificial intelligence models available today, choosing the right one for your specific project can feel overwhelming. This text-based course cuts through the noise, giving you a clear framework to evaluate and select the best foundation models for your needs. You will transition from being confused by AI terminology to confidently analyzing models based on size, cost, capabilities, and licensing. By understanding the trade-offs between proprietary and open-source options, you will make informed decisions that align with your project goals and budget. What you'll learn: - Understand the core concepts of generative AI, large language models, and tokenization - Evaluate models based on performance benchmarks, context window size, and latency - Compare the trade-offs between proprietary APIs and hosting open-source models - Apply structured evaluation criteria to match specific business needs with the right AI architecture - Explore modern implementation patterns, including basic prompt engineering and retrieval-augmented generation (RAG) - Analyze the cost implications and scaling factors of different model providers This course begins with foundational definitions of generative AI terminology before guiding you step-by-step through practical model evaluation frameworks. You will read real-world scenarios and complete written exercises designed to build your decision-making confidence. This course is designed for beginners, developers, and product managers who want to understand the modern AI landscape without needing a background in data science or machine learning. Start reading today to master the essential frameworks for selecting the perfect generative AI model for your next project.